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Becoming a more data-driven decision-maker can bring several benefits to your organization, enabling you to identify new opportunities to pursue and threats to abate. Rather than allowing subjective thinking to guide your business strategy, backing your decisions with data can empower your company to become more innovative and, ultimately, profitable.
If you’re new to data-driven decision-making, you might be wondering how data translates into business strategy. The answer lies in generating a hypothesis and verifying or rejecting it based on what various forms of data tell you.
Below is a look at hypothesis testing and the role it plays in helping businesses become more data-driven.
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To understand what hypothesis testing is, it’s important first to understand what a hypothesis is.
A hypothesis or hypothesis statement seeks to explain why something has happened, or what might happen, under certain conditions. It can also be used to understand how different variables relate to each other. Hypotheses are often written as if-then statements; for example, “If this happens, then this will happen.”
Hypothesis testing , then, is a statistical means of testing an assumption stated in a hypothesis. While the specific methodology leveraged depends on the nature of the hypothesis and data available, hypothesis testing typically uses sample data to extrapolate insights about a larger population.
When it comes to data-driven decision-making, there’s a certain amount of risk that can mislead a professional. This could be due to flawed thinking or observations, incomplete or inaccurate data , or the presence of unknown variables. The danger in this is that, if major strategic decisions are made based on flawed insights, it can lead to wasted resources, missed opportunities, and catastrophic outcomes.
The real value of hypothesis testing in business is that it allows professionals to test their theories and assumptions before putting them into action. This essentially allows an organization to verify its analysis is correct before committing resources to implement a broader strategy.
As one example, consider a company that wishes to launch a new marketing campaign to revitalize sales during a slow period. Doing so could be an incredibly expensive endeavor, depending on the campaign’s size and complexity. The company, therefore, may wish to test the campaign on a smaller scale to understand how it will perform.
In this example, the hypothesis that’s being tested would fall along the lines of: “If the company launches a new marketing campaign, then it will translate into an increase in sales.” It may even be possible to quantify how much of a lift in sales the company expects to see from the effort. Pending the results of the pilot campaign, the business would then know whether it makes sense to roll it out more broadly.
Related: 9 Fundamental Data Science Skills for Business Professionals
1. alternative hypothesis and null hypothesis.
In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis . Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis , on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.
For example, consider a company’s leadership team that historically and reliably sees $12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.
In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our flagship service by five percent, then we’ll see an increase in sales and realize revenues greater than $12 million in the next month.”
The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of $12 million, or might even decrease.
Check out the video below about the difference between an alternative and a null hypothesis, and subscribe to our YouTube channel for more explainer content.
Statistically speaking, if you were to run the same scenario 100 times, you’d likely receive somewhat different results each time. If you were to plot these results in a distribution plot, you’d see the most likely outcome is at the tallest point in the graph, with less likely outcomes falling to the right and left of that point.
With this in mind, imagine you’ve completed your hypothesis test and have your results, which indicate there may be a correlation between the variables you were testing. To understand your results' significance, you’ll need to identify a p-value for the test, which helps note how confident you are in the test results.
In statistics, the p-value depicts the probability that, assuming the null hypothesis is correct, you might still observe results that are at least as extreme as the results of your hypothesis test. The smaller the p-value, the more likely the alternative hypothesis is correct, and the greater the significance of your results.
When it’s time to test your hypothesis, it’s important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests , or one-tailed and two-tailed tests, respectively.
Typically, you’d leverage a one-sided test when you have a strong conviction about the direction of change you expect to see due to your hypothesis test. You’d leverage a two-sided test when you’re less confident in the direction of change.
To perform hypothesis testing in the first place, you need to collect a sample of data to be analyzed. Depending on the question you’re seeking to answer or investigate, you might collect samples through surveys, observational studies, or experiments.
A survey involves asking a series of questions to a random population sample and recording self-reported responses.
Observational studies involve a researcher observing a sample population and collecting data as it occurs naturally, without intervention.
Finally, an experiment involves dividing a sample into multiple groups, one of which acts as the control group. For each non-control group, the variable being studied is manipulated to determine how the data collected differs from that of the control group.
Hypothesis testing is a complex process involving different moving pieces that can allow an organization to effectively leverage its data and inform strategic decisions.
If you’re interested in better understanding hypothesis testing and the role it can play within your organization, one option is to complete a course that focuses on the process. Doing so can lay the statistical and analytical foundation you need to succeed.
Do you want to learn more about hypothesis testing? Explore Business Analytics —one of our online business essentials courses —and download our Beginner’s Guide to Data & Analytics .
Saul Mcleod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
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A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .
Hypotheses connect theory to data and guide the research process towards expanding scientific understanding
Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.
Alternative hypothesis.
The research hypothesis is often called the alternative or experimental hypothesis in experimental research.
It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.
The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).
A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:
In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.
It states that the results are not due to chance and are significant in supporting the theory being investigated.
The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.
The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.
It states results are due to chance and are not significant in supporting the idea being investigated.
The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.
Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.
This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.
A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.
It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.
For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.
A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)
It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.
For example, “Exercise increases weight loss” is a directional hypothesis.
The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.
Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.
It means that there should exist some potential evidence or experiment that could prove the proposition false.
However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.
For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.
Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.
All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.
In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.
We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.
If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.
Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.
Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).
Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:
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In software testing, there is an approach known as property-based testing that leverages the concept of formal specification of code behavior and focuses on asserting properties that hold true for a wide range of inputs rather than individual test cases.
Python is an open-source programming language that provides a Hypothesis library for property-based testing. Hypothesis testing in Python provides a framework for generating diverse and random test data, allowing development and testing teams to thoroughly test their code against a broad spectrum of inputs.
In this blog, we will explore the fundamentals of Hypothesis testing in Python using Selenium and Playwright. We’ll learn various aspects of Hypothesis testing, from basic usage to advanced strategies, and demonstrate how it can improve the robustness and reliability of the codebase.
TABLE OF CONTENTS
Decorators in hypothesis, strategies in hypothesis, setting up python environment for hypothesis testing, how to perform hypothesis testing in python, hypothesis testing in python with selenium and playwright.
Hypothesis is a property-based testing library that automates test data generation based on properties or invariants defined by the developers and testers.
In property-based testing, instead of specifying individual test cases, developers define general properties that the code should satisfy. Hypothesis then generates a wide range of input data to test these properties automatically.
Property-based testing using Hypothesis allows developers and testers to focus on defining the behavior of their code rather than writing specific test cases, resulting in more comprehensive testing coverage and the discovery of edge cases and unexpected behavior.
Writing property-based tests usually consists of deciding on guarantees our code should make – properties that should always hold, regardless of what the world throws at the code.
Examples of such guarantees can be:
Before we proceed further, it’s worthwhile to understand decorators in Python a bit since the Hypothesis library exposes decorators that we need to use to write tests.
In Python, decorators are a powerful feature that allows you to modify or extend the behavior of functions or classes without changing their source code. Decorators are essentially functions themselves, which take another function (or class) as input and return a new function (or class) with added functionality.
Decorators are denoted by the @ symbol followed by the name of the decorator function placed directly before the definition of the function or class to be modified.
Let us understand this with the help of an example:
In the example above, only authenticated users are allowed to create_post() . The logic to check authentication is wrapped in its own function, authenticate() .
This function can now be called using @authenticate before beginning a function where it’s needed & Python would automatically know that it needs to execute the code of authenticate() before calling the function.
If we no longer need the authentication logic in the future, we can simply remove the @authenticate line without disturbing the core logic. Thus, decorators are a powerful construct in Python that allows plug-n-play of repetitive logic into any function/method.
Now that we know the concept of Python decorators, let us understand the given decorators that which Hypothesis provides.
This decorator turns a test function that accepts arguments into a randomized test. It serves as the main entry point to the Hypothesis.
The @given decorator can be used to specify which arguments of a function should be parameterized over. We can use either positional or keyword arguments, but not a mixture of both.
.given(*_given_arguments, **_given_kwargs) |
Some valid declarations of the @given decorator are:
given(integers(), integers()) a(x, y): pass given(integers()) b(x, y): pass given(y=integers()) c(x, y): pass given(x=integers()) d(x, y): pass given(x=integers(), y=integers()) e(x, **kwargs): pass given(x=integers(), y=integers()) f(x, *args, **kwargs): pass SomeTest(TestCase): @given(integers()) def test_a_thing(self, x): pass |
Some invalid declarations of @given are:
given(integers(), integers(), integers()) g(x, y): pass given(integers()) h(x, *args): pass given(integers(), x=integers()) i(x, y): pass given() j(x, y): pass |
When writing production-grade applications, the ability of a Hypothesis to generate a wide range of input test data plays a crucial role in ensuring robustness.
However, there are certain inputs/scenarios the testing team might deem mandatory to be tested as part of every test run. Hypothesis has the @example decorator in such cases where we can specify values we always want to be tested. The @example decorator works for all strategies.
Let’s understand by tweaking the factorial test example.
The above test will always run for the input value 41 along with other custom-generated test data by the Hypothesis st.integers() function.
By now, we understand that the crux of the Hypothesis is to test a function for a wide range of inputs. These inputs are generated automatically, and the Hypothesis lets us configure the range of inputs. Under the hood, the strategy method takes care of the process of generating this test data of the correct data type.
Hypothesis offers a wide range of strategies such as integers, text, boolean, datetime, etc. For more complex scenarios, which we will see a bit later in this blog, the hypothesis also lets us set up composite strategies.
While not exhaustive, here is a tabular summary of strategies available as part of the Hypothesis library.
Strategy | Description |
---|---|
Generates none values. | |
Generates boolean values (True or False). | |
Generates integer values. | |
Generates floating-point values. | |
Generates unicode text strings. | |
Generates single unicode characters. | |
Generates lists of elements. | |
Generates tuples of elements. | |
Generates dictionaries with specified keys and values. | |
Generates sets of elements. | |
Generates binary data. | |
Generates datetime objects. | |
Generates timedelta objects. | |
Choose one of the given strategies with equal probability. | |
Chooses values from a given sequence with equal probability. | |
Generates lists of elements. | |
Generates date objects. | |
Generates datetime objects. | |
Generates a single value. | |
Generates strings that match a given regular expression. | |
Generates UUID objects. | |
Generates complex numbers. | |
Generates fraction objects. | |
Builds objects using a provided constructor and strategy for each argument. | |
Generates single unicode characters. | |
Generates unicode text strings. | |
Chooses values from a given sequence with equal probability. | |
Generates arbitrary data values. | |
Generates values that are shared between different parts of a test. | |
Generates recursively structured data. | |
Generates data based on the outcome of other strategies. |
Let’s see the steps to how to set up a test environment to perform Hypothesis testing in Python.
Our final project structure setup looks like below:
With the setup done, let us now understand Hypothesis testing in Python with various examples, starting with the introductory one and then working toward more complex ones.
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Let’s now start writing tests to understand how we can leverage the Hypothesis library to perform Python automation .
For this, let’s look at one test scenario to understand Hypothesis testing in Python.
Test Scenario:
Implementation:
This is what the initial implementation of the function looks like:
factorial(num: int) -> int: if num < 0: raise ValueError("Input must be > 0") fact = 1 for _ in range(1, num + 1): fact *= _ return fact |
It takes in an integer as an input. If the input is 0, it raises an error; if not, it uses the range() function to generate a list of numbers within, iterate over it, calculate the factorial, and return it.
Let’s now write a test using the Hypothesis library to test the above function:
hypothesis import given, strategies as st given(st.integers(min_value=1, max_value=30)) test_factorial(num: int): fact_num_result = factorial(num) fact_num_minus_one_result = factorial(num-1) result = fact_num_result / fact_num_minus_one_result assert num == result |
Code Walkthrough:
Let’s now understand the step-by-step code walkthrough for Hypothesis testing in Python.
Step 1: From the Hypothesis library, we import the given decorator and strategies method.
Step 2: Using the imported given and strategies, we set our test strategy of passing integer inputs within the range of 1 to 30 to the function under test using the min_value and max_value arguments.
Step 3: We write the actual test_factorial where the integer generated by our strategy is passed automatically by Hypothesis into the value num.
Using this value we call the factorial function once for value num and num – 1.
Next, we divide the factorial of num by the factorial of num -1 and assert if the result of the operation is equal to the original num.
Test Execution:
Let’s now execute our hypothesis test using the pytest -v -k “test_factorial” command.
And Hypothesis confirms that our function works perfectly for the given set of inputs, i.e., for integers from 1 to 30.
We can also view detailed statistics of the Hypothesis run by passing the argument –hypothesis-show-statistics to pytest command as:
-v --hypothesis-show-statistics -k "test_factorial" |
The difference between the reuse and generate phase in the output above is explained below:
This phase typically has a very short runtime, as it involves reusing existing test data or shrinking failing examples. The output provides statistics about the typical runtimes and the number of passing, failing, and invalid examples encountered during this phase.
The output provides statistics about the typical runtimes and the number of passing, failing, and invalid examples generated during this phase. While this helped us understand what passing tests look like with a Hypothesis, it’s also worthwhile to understand how a Hypothesis can catch bugs in the code.
Let’s rewrite the factorial() function with an obvious bug, i.e., remove the check for when the input value is 0.
factorial(num: int) -> int: # if num < 0: # raise ValueError("Number must be >= 0") fact = 1 for _ in range(1, num + 1): fact *= _ return fact |
We also tweak the test to remove the min_value and max_value arguments.
given(st.integers()) test_factorial(num: int): fact_num_result = factorial(num) fact_num_minus_one_result = factorial(num-1) result = int(fact_num_result / fact_num_minus_one_result) assert num == result |
Let us now rerun the test with the same command:
-v --hypothesis-show-statistics -k test_factorial |
pytest -v --hypothesis-show-statistics -k test_factorial |
We can clearly see how Hypothesis has caught the bug immediately, which is shown in the above output. Hypothesis presents the input that resulted in the failing test under the Falsifying example section of the output.
So far, we’ve performed Hypothesis testing locally. This works nicely for unit tests , but when setting up automation for building more robust and resilient test suites, we can leverage a cloud grid like LambdaTest that supports automation testing tools like Selenium and Playwright.
LambdaTest is an AI-powered test orchestration and execution platform that enables developers and testers to perform automation testing with Selenium and Playwright at scale. It provides a remote test lab of 3000+ real environments.
Selenium is an open-source suite of tools and libraries for web automation . When combined with a cloud grid, it can help you perform Hypothesis testing in Python with Selenium at scale.
Let’s look at one test scenario to understand Hypothesis testing in Python with Selenium.
The code to set up a connection to LambdaTest Selenium Grid is stored in a crossbrowser_selenium.py file.
selenium import webdriver selenium.webdriver.chrome.options import Options selenium.webdriver.common.keys import Keys time import sleep urllib3 warnings os selenium.webdriver import ChromeOptions selenium.webdriver import FirefoxOptions selenium.webdriver.remote.remote_connection import RemoteConnection hypothesis.strategies import integers dotenv import load_dotenv () = os.getenv('LT_USERNAME', None) = os.getenv('LT_ACCESS_KEY', None) CrossBrowserSetup: global web_driver def __init__(self): global remote_url urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) remote_url = "https://" + str(username) + ":" + str(access_key) + "@hub.lambdatest.com/wd/hub" def add(self, browsertype): if (browsertype == "Firefox"): ff_options = webdriver.FirefoxOptions() ff_options.browser_version = "latest" ff_options.platform_name = "Windows 11" lt_options = {} lt_options["build"] = "Build: FF: Hypothesis Testing with Selenium & Pytest" lt_options["project"] = "Project: FF: Hypothesis Testing withSelenium & Pytest" lt_options["name"] = "Test: FF: Hypothesis Testing with Selenium & Pytest" lt_options["browserName"] = "Firefox" lt_options["browserVersion"] = "latest" lt_options["platformName"] = "Windows 11" lt_options["console"] = "error" lt_options["w3c"] = True lt_options["headless"] = False ff_options.set_capability('LT:Options', lt_options) web_driver = webdriver.Remote( command_executor = remote_url, options = ff_options ) self.driver = web_driver self.driver.get("https://www.lambdatest.com") sleep(1) if web_driver is not None: web_driver.execute_script("lambda-status=passed") web_driver.quit() return True else: return False |
The test_selenium.py contains code to test the Hypothesis that tests will only run on the Firefox browser.
hypothesis import given, settings hypothesis import given, example hypothesis.strategies as strategy src.crossbrowser_selenium import CrossBrowserSetup settings(deadline=None) given(strategy.just("Firefox")) test_add(browsertype_1): cbt = CrossBrowserSetup() assert True == cbt.add(browsertype_1) |
Let’s now understand the step-by-step code walkthrough for Hypothesis testing in Python using Selenium Grid.
Step 1: We import the necessary Selenium methods to initiate a connection to LambdaTest Selenium Grid.
The FirefoxOptions() method is used to configure the setup when connecting to LambdaTest Selenium Grid using Firefox.
Step 2: We use the load_dotenv package to access the LT_ACCESS_KEY required to access the LambdaTest Selenium Grid, which is stored in the form of environment variables.
The LT_ACCESS_KEY can be obtained from your LambdaTest Profile > Account Settings > Password & Security .
Step 3: We initialize the CrossBrowserSetup class, which prepares the remote connection URL using the username and access_key.
Step 4: The add() method is responsible for checking the browsertype and then setting the capabilities of the LambdaTest Selenium Grid.
LambdaTest offers a variety of capabilities, such as cross browser testing , which means we can test on various operating systems such as Windows, Linux, and macOS and multiple browsers such as Chrome, Firefox, Edge, and Safari.
For the purpose of this blog, we will be testing that connection to the LambdaTest Selenium Grid should only happen if the browsertype is Firefox.
Step 5: If the connection to LambdaTest happens, the add() returns True ; else, it returns False .
Let’s now understand a step-by-step walkthrough of the test_selenium.py file.
Step 1: We set up the imports of the given decorator and the Hypothesis strategy. We also import the CrossBrowserSetup class.
Step 2: @setting(deadline=None) ensures the test doesn’t timeout if the connection to the LambdaTest Grid takes more time.
We use the @given decorator to set the strategy to just use Firefox as an input to the test_add() argument broswertype_1. We then initialize an instance of the CrossBrowserSetup class & call the add() method using the broswertype_1 & assert if it returns True .
The commented strategy @given(strategy.just(‘Chrome’)) is to demonstrate that the add() method, when called with Chrome, returns False .
Let’s now run the test using pytest -k “test_hypothesis_selenium.py”.
We can see that the test has passed, and the Web Automation Dashboard reflects that the connection to the Selenium Grid has been successful.
On opening one of the execution runs, we can see a detailed step-by-step test execution.
Playwright is a popular open-source tool for end-to-end testing developed by Microsoft. When combined with a cloud grid, it can help you perform Hypothesis testing in Python at scale.
Let’s look at one test scenario to understand Hypothesis testing in Python with Playwright.
website. |
os dotenv import load_dotenv playwright.sync_api import expect, sync_playwright hypothesis import given, strategies as st subprocess urllib json () = { 'browserName': 'Chrome', # Browsers allowed: `Chrome`, `MicrosoftEdge`, `pw-chromium`, `pw-firefox` and `pw-webkit` 'browserVersion': 'latest', 'LT:Options': { 'platform': 'Windows 11', 'build': 'Playwright Hypothesis Demo Build', 'name': 'Playwright Locators Test For Windows 11 & Chrome', 'user': os.getenv('LT_USERNAME'), 'accessKey': os.getenv('LT_ACCESS_KEY'), 'network': True, 'video': True, 'visual': True, 'console': True, 'tunnel': False, # Add tunnel configuration if testing locally hosted webpage 'tunnelName': '', # Optional 'geoLocation': '', # country code can be fetched from https://www.lambdatest.com/capabilities-generator/ } interact_with_lambdatest(quantity): with sync_playwright() as playwright: playwrightVersion = str(subprocess.getoutput('playwright --version')).strip().split(" ")[1] capabilities['LT:Options']['playwrightClientVersion'] = playwrightVersion lt_cdp_url = 'wss://cdp.lambdatest.com/playwright?capabilities=' + urllib.parse.quote(json.dumps(capabilities)) browser = playwright.chromium.connect(lt_cdp_url) page = browser.new_page() page.goto("https://ecommerce-playground.lambdatest.io/") page.get_by_role("button", name="Shop by Category").click() page.get_by_role("link", name="MP3 Players").click() page.get_by_role("link", name="HTC Touch HD HTC Touch HD HTC Touch HD HTC Touch HD").click() page.get_by_role("button", name="Add to Cart").click(click_count=quantity) page.get_by_role("link", name="Checkout ").first.click() unit_price = float(page.get_by_role("cell", name="$146.00").first.inner_text().replace("$","")) page.evaluate("_ => {}", "lambdatest_action: {\"action\": \"setTestStatus\", \"arguments\": {\"status\":\"" + "Passed" + "\", \"remark\": \"" + "pass" + "\"}}" ) page.close() total_price = quantity * unit_price return total_price = st.integers(min_value=1, max_value=10) given(quantity=quantity_strategy) test_website_interaction(quantity): assert interact_with_lambdatest(quantity) == quantity * 146.00 |
Let’s now understand the step-by-step code walkthrough for Hypothesis testing in Python using Playwright Grid.
Step 1: To connect to the LambdaTest Playwright Grid, we need a Username and Access Key, which can be obtained from the Profile page > Account Settings > Password & Security.
We use the python-dotenv module to load the Username and Access Key, which are stored as environment variables.
The capabilities dictionary is used to set up the Playwright Grid on LambdaTest.
We configure the Grid to use Windows 11 and the latest version of Chrome.
Step 3: The function interact_with_lambdatest interacts with the LambdaTest eCommerce Playground website to simulate adding a product to the cart and proceeding to checkout.
It starts a Playwright session and retrieves the version of the Playwright being used. The LambdaTest CDP URL is created with the appropriate capabilities. It connects to the Chromium browser instance on LambdaTest.
A new page instance is created, and the LambdaTest eCommerce Playground website is navigated. The specified product is added to the cart by clicking through the required buttons and links. The unit price of the product is extracted from the web page.
The browser page is then closed.
Step 4: We define a Hypothesis strategy quantity_strategy using st.integers to generate random integers representing product quantities. The generated integers range from 1 to 10
Using the @given decorator from the Hypothesis library, we define a property-based test function test_website_interaction that takes a quantity parameter generated by the quantity_strategy .
Inside the test function, we use the interact_with_lambdatest function to simulate interacting with the website and calculate the total price based on the generated quantity.
We assert that the total_price returned by interact_with_lambdatest matches the expected value calculated as quantity * 146.00.
Let’s now run the test on the Playwright Cloud Grid using pytest -v -k “test_hypothesis_playwright.py ”
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In the previous test scenario, we saw a simple example where we used the integer() strategy available as part of the Hypothesis. Let’s now understand another strategy, the date() strategy, which can be effectively used to test date-based functions.
Also, the output of the Hypothesis run can be customized to produce detailed results. Often, we may wish to see an even more verbose output when executing a Hypothesis test.
To do so, we have two options: either use the @settings decorator or use the –hypothesis-verbosity=<verbosity_level> when performing pytest testing .
hypothesis import Verbosity,settings, given, strategies as st datetime import datetime, timedelta generate_expiry_alert(expiry_date): current_date = datetime.now().date() days_until_expiry = (expiry_date - current_date).days return days_until_expiry <= 45 given(expiry_date=st.dates()) settings(verbosity=Verbosity.verbose, max_examples=1000) test_expiry_alert_generation(expiry_date): alert_generated = generate_expiry_alert(expiry_date) # Check if the alert is generated correctly based on the expiry date days_until_expiry = (expiry_date - datetime.now().date()).days expected_alert = days_until_expiry <= 45 assert alert_generated == expected_alert |
Let’s now understand the code step-by-step.
Step 1: The function generate_expiry_alert() , which takes in an expiry_date as input and returns a boolean depending on whether the difference between the current date and expiry_date is less than or equal to 45 days.
Step 2: To ensure we test the generate_expiry_alert() for a wide range of date inputs, we use the date() strategy.
We also enable verbose logging and set the max_examples=1000 , which requests Hypothesis to generate 1000 date inputs at the max.
Step 3: On the inputs generated by Hypothesis in Step 3, we call the generate_expiry_alert() function and store the returned boolean in alert_generated.
We then compare the value returned by the function generate_expiry_alert() with a locally calculated copy and assert if the match.
We execute the test using the below command in the verbose mode, which allows us to see the test input dates generated by the Hypothesis.
-s --hypothesis-show-statistics --hypothesis-verbosity=debug -k "test_expiry_alert_generation" |
As we can see, Hypothesis ran 1000 tests, 2 with reused data and 998 with unique newly generated data, and found no issues with the code.
Now, imagine the trouble we would have had to take to write 1000 tests manually using traditional example-based testing.
So far, we’ve been using simple standalone examples to demo the power of Hypothesis. Let’s now move on to more complicated scenarios.
website offers customer rewards points. A class tracks the customer reward points and their spending. class. |
The implementation of the UserRewards class is stored in a user_rewards.py file for better readability.
UserRewards: def __init__(self, initial_points): self.reward_points = initial_points def get_reward_points(self): return self.reward_points def spend_reward_points(self, spent_points): if spent_points<= self.reward_points: self.reward_points -= spent_points return True else: return False |
The tests for the UserRewards class are stored in test_user_rewards.py .
hypothesis import given, strategies as st src.user_rewards import UserRewards = st.integers(min_value=0, max_value=1000) given(initial_points=reward_points_strategy) test_get_reward_points(initial_points): user_rewards = UserRewards(initial_points) assert user_rewards.get_reward_points() == initial_points given(initial_points=reward_points_strategy, spend_amount=st.integers(min_value=0, max_value=1000)) test_spend_reward_points(initial_points, spend_amount): user_rewards = UserRewards(initial_points) remaining_points = user_rewards.get_reward_points() if spend_amount <= initial_points: assert user_rewards.spend_reward_points(spend_amount) remaining_points -= spend_amount else: assert not user_rewards.spend_reward_points(spend_amount) assert user_rewards.get_reward_points() == remaining_points |
Let’s now understand what is happening with both the class file and the test file step-by-step, starting first with the UserReward class.
Step 1: The class takes in a single argument initial_points to initialize the object.
Step 2: The get_reward_points() function returns the customers current reward points.
Step 3: The spend_reward_points() takes in the spent_points as input and returns True if spent_points are less than or equal to the customer current point balance and updates the customer reward_points by subtracting the spent_points , else it returns False .
That is it for our simple UserRewards class. Next, we understand what’s happening in the test_user_rewards.py step-by-step.
Step 1: We import the @given decorator and strategies from Hypothesis and the UserRewards class.
Step 2: Since reward points will always be integers, we use the integer() Hypothesis strategy to generate 1000 sample inputs starting with 0 and store them in a reward_points_strategy variable.
Step 3: Use the rewards_point_strategy as an input we run the test_get_reward_points() for 1000 samples starting with value 0.
For each input, we initialize the UserRewards class and assert that the method get_reward_points() returns the same value as the initial_points .
Step 4: To test the spend_reward_points() function, we generate two sets of sample inputs first, an initial reward_points using the reward_points_strategy we defined in Step 2 and a spend_amount which simulates spending of points.
Step 5: Write the test_spend_reward_points , which takes in the initial_points and spend_amount as arguments and initializes the UserRewards class with initial_point .
We also initialize a remaining_points variable to track the points remaining after the spend.
Step 6: If the spend_amount is less than the initial_points allocated to the customer, we assert if spend_reward_points returns True and update the remaining_points else, we assert spend_reward_points returns False .
Step 7: Lastly, we assert if the final remaining_points are correctly returned by get_rewards_points , which should be updated after spending the reward points.
Let’s now run the test and see if Hypothesis is able to find any bugs in the code.
-s --hypothesis-show-statistics --hypothesis-verbosity=debug -k "test_user_rewards" |
To test if the Hypothesis indeed works, let’s make a small change to the UserRewards by commenting on the logic to deduct the spent_points in the spend_reward_points() function.
We run the test suite again using the command pytest -s –hypothesis-show-statistics -k “test_user_rewards “.
This time, the Hypothesis highlights the failures correctly.
Thus, we can catch any bugs and potential side effects of code changes early, making it perfect for unit testing and regression testing .
To understand composite strategies a bit more, let’s now test the shopping cart functionality and see how composite strategy can help write robust tests for even the most complicated of real-world scenarios.
and which handles the shopping cart feature of the website. |
Let’s view the implementation of the ShoppingCart class written in the shopping_cart.py file.
random enum import Enum, auto Item(Enum): """Item type""" LUNIX_CAMERA = auto() IMAC = auto() HTC_TOUCH = auto() CANNON_EOS = auto() IPOD_TOUCH = auto() APPLE_VISION_PRO = auto() COFMACBOOKFEE = auto() GALAXY_S24 = auto() def __str__(self): return self.name.upper() ShoppingCart: def __init__(self): """ "" self.items = {} def add_item(self, item: Item, price: int | float, quantity: int = 1) -> None: """ "" if item.name in self.items: self.items[item.name]["quantity"] += quantity else: self.items[item.name] = {"price": price, "quantity": quantity} def remove_item(self, item: Item, quantity: int = 1) -> None: """ "" if item.name in self.items: if self.items[item.name]["quantity"] <= quantity: del self.items[item.name] else: self.items[item.name]["quantity"] -= quantity def get_total_price(self): total_price = 0 for item in self.items.values(): total_price += item["price"] * item["quantity"] return total_price |
Let’s now view the tests written to verify the correct behavior of all aspects of the ShoppingCart class stored in a separate test_shopping_cart.py file.
typing import Callable hypothesis import given, strategies as st hypothesis.strategies import SearchStrategy src.shopping_cart import ShoppingCart, Item st.composite items_strategy(draw: Callable[[SearchStrategy[Item]], Item]): return draw(st.sampled_from(list(Item))) st.composite price_strategy(draw: Callable[[SearchStrategy[int]], int]): return draw(st.integers(min_value=1, max_value=100)) st.composite qty_strategy(draw: Callable[[SearchStrategy[int]], int]): return draw(st.integers(min_value=1, max_value=10)) given(items_strategy(), price_strategy(), qty_strategy()) test_add_item_hypothesis(item, price, quantity): cart = ShoppingCart() # Add items to cart cart.add_item(item=item, price=price, quantity=quantity) # Assert that the quantity of items in the cart is equal to the number of items added assert item.name in cart.items assert cart.items[item.name]["quantity"] == quantity given(items_strategy(), price_strategy(), qty_strategy()) test_remove_item_hypothesis(item, price, quantity): cart = ShoppingCart() print("Adding Items") # Add items to cart cart.add_item(item=item, price=price, quantity=quantity) cart.add_item(item=item, price=price, quantity=quantity) print(cart.items) # Remove item from cart print(f"Removing Item {item}") quantity_before = cart.items[item.name]["quantity"] cart.remove_item(item=item) quantity_after = cart.items[item.name]["quantity"] # Assert that if we remove an item, the quantity of items in the cart is equal to the number of items added - 1 assert quantity_before == quantity_after + 1 given(items_strategy(), price_strategy(), qty_strategy()) test_calculate_total_hypothesis(item, price, quantity): cart = ShoppingCart() # Add items to cart cart.add_item(item=item, price=price, quantity=quantity) cart.add_item(item=item, price=price, quantity=quantity) # Remove item from cart cart.remove_item(item=item) # Calculate total total = cart.get_total_price() assert total == cart.items[item.name]["price"] * cart.items[item.name]["quantity"] |
Code Walkthrough of ShoppingCart class:
Let’s now understand what is happening in the ShoppingCart class step-by-step.
Step 1: We import the Python built-in Enum class and the auto() method.
The auto function within the Enum module automatically assigns sequential integer values to enumeration members, simplifying the process of defining enumerations with incremental values.
We define an Item enum corresponding to items available for sale on the LambdaTest eCommerce Playground website.
Step 2: We initialize the ShoppingCart class with an empty dictionary of items.
Step 3: The add_item() method takes in the item, price, and quantity as input and adds it to the shopping cart state held in the item dictionary.
Step 4: The remove_item() method takes in an item and quantity and removes it from the shopping cart state indicated by the item dictionary.
Step 5: The get_total_price() method iterates over the item dictionary, multiples the quantity by price, and returns the total_price of items in the cart.
Code Walkthrough of test_shopping_cart:
Let’s now understand step-by-step the tests written to ensure the correct working of the ShoppingCart class.
Step 1: First, we set up the imports, including the @given decorator, strategies, and the ShoppingCart class and Item enum.
The SearchStrategy is one of the various strategies on offer as part of the Hypothesis. It represents a set of rules for generating valid inputs to test a specific property or behavior of a function or program.
Step 2: We use the @st.composite decorator to define a custom Hypothesis strategy named items_strategy. This strategy takes a single argument, draw, which is a callable used to draw values from other strategies.
The st.sampled_from strategy randomly samples values from a given iterable. Within the strategy, we use draw(st.sampled_from(list(Item))) to draw a random Item instance from a list of all enum members.
Each time the items_strategy is used in a Hypothesis test, it will generate a random instance of the Item enum for testing purposes.
Step 3: The price_strategy runs on similar logic as the item_strategy but generates an integer value between 1 and 100.
Step 4: The qty_strategy runs on similar logic as the item_strategy but generates an integer value between 1 and 10.
Step 5: We use the @given decorator from the Hypothesis library to define a property-based test.
The items_strategy() , price_strategy() , and qty_strategy() functions are used to generate random values for the item, price, and quantity parameters, respectively.
Inside the test function, we create a new instance of a ShoppingCart .
We then add an item to the cart using the generated values for item, price, and quantity.
Finally, we assert that the item was successfully added to the cart and that the quantity matches the generated quantity.
Step 6: We use the @given decorator from the Hypothesis library to define a property-based test.
The items_strategy(), price_strategy() , and qty_strategy() functions are used to generate random values for the item, price, and quantity parameters, respectively.
Inside the test function, we create a new instance of a ShoppingCart . We then add the same item to the cart twice to simulate two quantity additions to the cart.
We remove one instance of the item from the cart. After that, we compare the item quantity before and after removal to ensure it decreases by 1.
The test verifies the behavior of the remove_item() method of the ShoppingCart class by testing it with randomly generated inputs for item, price , and quantity.
Step 7: We use the @given decorator from the Hypothesis library to define a property-based test.
The items_strategy(), price_strategy(), and qty_strategy() functions are used to generate random values for the item, price, and quantity parameters, respectively.
We add the same item to the cart twice to ensure it’s present, then remove one instance of the item from the cart. After that, we calculate the total price of items remaining in the cart.
Finally, we assert that the total price matches the price of one item times its remaining quantity.
The test verifies the correctness of the get_total_price() method of the ShoppingCart class by testing it with randomly generated inputs for item, price , and quantity .
Let’s now run the test using the command pytest –hypothesis-show-statistics -k “test_shopping_cart”.
We can verify that Hypothesis was able to find no issues with the ShoppingCart class.
Let’s now amend the price_strategy and qty_strategy to remove the min_value and max_value arguments.
And rerun the test pytest -k “test_shopping_cart” .
The tests run clearly reveal that we have bugs with respect to handling scenarios when quantity and price are passed as 0.
This also reveals the fact that setting the test inputs correctly to ensure we do comprehensive testing is key to writing robots and resilient tests.
Choosing min_val and max_val should only be done when we know beforehand the bounds of inputs the function under test will receive. If we are unsure what the inputs are, maybe it’s important to come up with the right strategies based on the behavior of the function under test.
In this blog we have seen in detail how Hypothesis testing in Python works using the popular Hypothesis library. Hypothesis testing falls under property-based testing and is much better than traditional testing in handling edge cases.
We also explored Hypothesis strategies and how we can use the @composite decorator to write custom strategies for testing complex functionalities.
We also saw how Hypothesis testing in Python can be performed with popular test automation frameworks like Selenium and Playwright. In addition, by performing Hypothesis testing in Python with LambdaTest on Cloud Grid, we can set up effective automation tests to enhance our confidence in the code we’ve written.
There are three main types of hypothesis tests based on the direction of the alternative hypothesis:
Hypothesis testing is a statistical approach used to evaluate the performance and validity of machine learning models. It helps us determine if a pattern observed in the training data likely holds true for unseen data (generalizability).
Jaydeep is a software engineer with 10 years of experience, most recently developing and supporting applications written in Python. He has extensive with shell scripting and is also an AI/ML enthusiast. He is also a tech educator, creating content on Twitter, YouTube, Instagram, and LinkedIn. Link to his YouTube channel- https://www.youtube.com/@jaydeepkarale
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Here's a look at the foundation of doing science — the scientific method.
Hypothesis, theory and law, a brief history of science, additional resources, bibliography.
Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe.
The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.
When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .
"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."
The steps of the scientific method go something like this, according to Highline College :
Some key underpinnings to the scientific method:
The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed.
A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"
Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.
This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.
The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.
1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.
1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.
1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.
1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.
1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.
1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.
1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.
In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .
2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .
Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia
University of California, Berkeley, "Understanding Science: An Overview." 2022. https://undsci.berkeley.edu/article/0_0_0/intro_01
Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm
North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html
University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045
Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group
The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml
Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/
Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk
National Human Genome Research Institute, "Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone
National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What
Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.
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The Difference Between Hypothesis and Theory
A hypothesis is an assumption, an idea that is proposed for the sake of argument so that it can be tested to see if it might be true.
In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.
A hypothesis is usually tentative; it's an assumption or suggestion made strictly for the objective of being tested.
A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, it is understood to be more likely to be true than a hypothesis is.
In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch, with theory being the more common choice.
Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.
The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)
This mistake is one of projection: since we use theory in general to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.
The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”
While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."
hypothesis , theory , law mean a formula derived by inference from scientific data that explains a principle operating in nature.
hypothesis implies insufficient evidence to provide more than a tentative explanation.
theory implies a greater range of evidence and greater likelihood of truth.
law implies a statement of order and relation in nature that has been found to be invariable under the same conditions.
These examples are programmatically compiled from various online sources to illustrate current usage of the word 'hypothesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.
Greek, from hypotithenai to put under, suppose, from hypo- + tithenai to put — more at do
1641, in the meaning defined at sense 1a
This is the Difference Between a...
In scientific reasoning, they're two completely different things
hypothermia
hypothesize
“Hypothesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/hypothesis. Accessed 12 Jun. 2024.
Kids definition of hypothesis, medical definition, medical definition of hypothesis, more from merriam-webster on hypothesis.
Nglish: Translation of hypothesis for Spanish Speakers
Britannica English: Translation of hypothesis for Arabic Speakers
Britannica.com: Encyclopedia article about hypothesis
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An Introduction to Statistics class in Davies County, KY conducted a hypothesis test at the local high school (a medium sized-approximately 1,200 students-small city demographic) to determine if the local high school's percentage was lower. One hundred fifty students were chosen at random and surveyed.
Table of contents. Step 1: State your null and alternate hypothesis. Step 2: Collect data. Step 3: Perform a statistical test. Step 4: Decide whether to reject or fail to reject your null hypothesis. Step 5: Present your findings. Other interesting articles. Frequently asked questions about hypothesis testing.
Unit 12: Significance tests (hypothesis testing) Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You'll also see how we use p-values ...
If the biologist set her significance level \(\alpha\) at 0.05 and used the critical value approach to conduct her hypothesis test, she would reject the null hypothesis if her test statistic t* were less than -1.6939 (determined using statistical software or a t-table):s-3-3. Since the biologist's test statistic, t* = -4.60, is less than -1.6939, the biologist rejects the null hypothesis.
Photo from StepUp Analytics. Hypothesis testing is a method of statistical inference that considers the null hypothesis H₀ vs. the alternative hypothesis Ha, where we are typically looking to assess evidence against H₀. Such a test is used to compare data sets against one another, or compare a data set against some external standard. The former being a two sample test (independent or ...
S.3 Hypothesis Testing. In reviewing hypothesis tests, we start first with the general idea. Then, we keep returning to the basic procedures of hypothesis testing, each time adding a little more detail. The general idea of hypothesis testing involves: Making an initial assumption. Collecting evidence (data).
Hypothesis testing is an indispensable tool in data science, allowing us to make data-driven decisions with confidence. By understanding its principles, conducting tests properly, and considering real-world applications, you can harness the power of hypothesis testing to unlock valuable insights from your data.
Simple hypothesis testing. Niels has a Magic 8 -Ball, which is a toy used for fortune-telling or seeking advice. To consult the ball, you ask the ball a question and shake it. One of 5 different possible answers then appears at random in the ball. Niels sensed that the ball answers " Ask again later " too frequently.
Below these are summarized into six such steps to conducting a test of a hypothesis. Set up the hypotheses and check conditions: Each hypothesis test includes two hypotheses about the population. One is the null hypothesis, notated as H 0, which is a statement of a particular parameter value. This hypothesis is assumed to be true until there is ...
In contrast, a research hypothesis is a research question for a researcher that has to be proven correct or incorrect through investigation. What is Hypothesis Testing? Hypothesis testing is a scientific method used for making a decision and drawing conclusions by using a statistical approach. It is used to suggest new ideas by testing theories ...
View Solution to Question 1. Question 2. A professor wants to know if her introductory statistics class has a good grasp of basic math. Six students are chosen at random from the class and given a math proficiency test. The professor wants the class to be able to score above 70 on the test. The six students get the following scores:62, 92, 75 ...
23.1 How Hypothesis Tests Are Reported in the News 1. Determine the null hypothesis and the alternative hypothesis. 2. Collect and summarize the data into a test statistic. 3. Use the test statistic to determine the p-value. 4. The result is statistically significant if the p-value is less than or equal to the level of significance.
Stats 2 Hypothesis Testing Questions . Stats 2 Hypothesis Testing Answers . 6 In previous years, the marks obtained in a French test by students attending Topnotch College have been modelled satisfactorily by a normal distribution with a mean of 65 and a standard deviation of 9.
A hypothesis test consists of five steps: 1. State the hypotheses. State the null and alternative hypotheses. These two hypotheses need to be mutually exclusive, so if one is true then the other must be false. 2. Determine a significance level to use for the hypothesis. Decide on a significance level.
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
Carry out an appropriate statistical test and interpret your findings. ANSWER . Yes, a paired t-test suggests that the average difference in hours slept (Dalmane - Halcion) = 0.32 is statistically significant (one sided p-value = .018). SOLUTION . This question is asking for a hypothesis test of the equality of two means in the setting of ...
3. One-Sided vs. Two-Sided Testing. When it's time to test your hypothesis, it's important to leverage the correct testing method. The two most common hypothesis testing methods are one-sided and two-sided tests, or one-tailed and two-tailed tests, respectively. Typically, you'd leverage a one-sided test when you have a strong conviction ...
At this point, hypothesis testing structures the problems so that we can use statistical evidence to test these claims. So we can check whether or not the claim is valid. In this article, I want to show hypothesis testing with Python on several questions step-by-step. But before, let me explain the hypothesis testing process briefly.
Hypothesis Testing www.naikermaths.com 10. (a) Define the critical region of a test statistic.(2) A discrete random variable X has a Binomial distribution B(30, p).A single observation is used to test H 0: p = 0.3 against H 1: p ≠ 0.3 (b) Using a 1% level of significance find the critical region of this test.You should state the probability of rejection in each tail which should be as close ...
In statistical hypothesis testing, you reject the null hypothesis when the p-value is less than or equal to the significance level (α) you set before conducting your test. The significance level is the probability of rejecting the null hypothesis when it is true. Commonly used significance levels are 0.01, 0.05, and 0.10.
Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Bibliography. A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an ...
Hypothesis Testing in Python With Selenium and Playwright. So far, we've performed Hypothesis testing locally. This works nicely for unit tests, but when setting up automation for building more robust and resilient test suites, we can leverage a cloud grid like LambdaTest that supports automation testing tools like Selenium and Playwright.
The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory.
hypothesis: [noun] an assumption or concession made for the sake of argument. an interpretation of a practical situation or condition taken as the ground for action.
Math; Statistics and Probability; Statistics and Probability questions and answers; What does a p-value indicate in hypothesis testing?a) The probability of making a Type I errorb) The probability of making a Type II errorc) The probability of obtaining the observed results by chance, assuming the null hypothesis is trued) The probability of obtaining the observed results by chance ...