define hypothesis ppt

• Bipolar Disorder
• Therapy Center
• When To See a Therapist
• Types of Therapy
• Best Online Therapy
• Best Couples Therapy
• Best Family Therapy
• Managing Stress
• Sleep and Dreaming
• Understanding Emotions
• Self-Improvement
• Healthy Relationships
• Student Resources
• Personality Types
• Guided Meditations
• Verywell Mind Insights
• 2024 Verywell Mind 25
• Mental Health in the Classroom
• Editorial Process
• Meet Our Review Board
• Crisis Support

How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

• My presentations

Auth with social network:

Download presentation

We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!

Presentation is loading. Please wait.

Introduction to Hypothesis Testing

Published by Emily Payne Modified over 10 years ago

Similar presentations

Presentation on theme: "Introduction to Hypothesis Testing"— Presentation transcript:

Introductory Mathematics & Statistics for Business

Hypothesis Testing W&W, Chapter 9.

HYPOTHESIS TESTING. Purpose The purpose of hypothesis testing is to help the researcher or administrator in reaching a decision concerning a population.

Chapter 7 Hypothesis Testing

Our goal is to assess the evidence provided by the data in favor of some claim about the population. Section 6.2Tests of Significance.

Anthony Greene1 Simple Hypothesis Testing Detecting Statistical Differences In The Simplest Case:  and  are both known I The Logic of Hypothesis Testing:

Hypothesis Testing A hypothesis is a claim or statement about a property of a population (in our case, about the mean or a proportion of the population)

Statistical Issues in Research Planning and Evaluation

COURSE: JUST 3900 INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE Instructor: Dr. John J. Kerbs, Associate Professor Joint Ph.D. in Social Work and Sociology.

Hypothesis testing Week 10 Lecture 2.

HYPOTHESIS TESTING Four Steps Statistical Significance Outcomes Sampling Distributions.

Behavioural Science II Week 1, Semester 2, 2002

Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.

Cal State Northridge  320 Ainsworth Sampling Distributions and Hypothesis Testing.

Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~

Introduction to Hypothesis Testing CJ 526 Statistical Analysis in Criminal Justice.

Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 9-1 Chapter 9 Fundamentals of Hypothesis Testing: One-Sample Tests Basic Business Statistics.

PY 427 Statistics 1Fall 2006 Kin Ching Kong, Ph.D Lecture 6 Chicago School of Professional Psychology.

About project

© 2024 SlidePlayer.com Inc. All rights reserved.

Got any suggestions?

We want to hear from you! Send us a message and help improve Slidesgo

Top searches

Trending searches

holy spirit

35 templates

11 templates

business pitch

598 templates

ai technology

169 templates

21 templates

environmental science

36 templates

Scientific Method Lesson for Middle School: Writing a Hypothesis

It seems that you like this template, scientific method lesson for middle school: writing a hypothesis presentation, free google slides theme, powerpoint template, and canva presentation template.

We say: If you achieve mind-blowing success in your next project, it is likely due to the foundation established by utilizing this high-quality template! Do you want to test this hypothesis? Great, here’s how: Download this slide deck, edit it in Google Slides or PowerPoint, and watch your students’ reaction when you explain to them all there is to know about formulating scientific hypotheses. Oh, and please don’t forget to let us know the results for verification!

Features of this template

• 100% editable and easy to modify
• 36 different slides to impress your audience
• Contains easy-to-edit graphics such as graphs, maps, tables, timelines and mockups
• Includes 500+ icons and Flaticon’s extension for customizing your slides
• Designed to be used in Google Slides, Canva, and Microsoft PowerPoint
• 16:9 widescreen format suitable for all types of screens
• Includes information about fonts, colors, and credits of the resources used

How can I use the template?

Am I free to use the templates?

How to attribute?

Attribution required If you are a free user, you must attribute Slidesgo by keeping the slide where the credits appear. How to attribute?

Related posts on our blog.

How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides

How to Change Layouts in PowerPoint

How to Change the Slide Size in Google Slides

Related presentations.

Premium template

Unlock this template and gain unlimited access

Chapter 7 Hypothesis Testing

Aug 14, 2012

860 likes | 1.96k Views

7-1 Basics of Hypothesis Testing 7-2 Testing a Claim about a Mean: Large Samples 7-3 Testing a Claim about a Mean: Small Samples 7-4 Testing a Claim about a Proportion 7- 5 Testing a Claim about a Standard     Deviation (will cover with chap 8). Chapter 7 Hypothesis Testing. 7-1.

Share Presentation

• standard deviation s
• exam questions
• significance level
• large samples

Presentation Transcript

7-1 Basics of Hypothesis Testing 7-2 Testing a Claim about a Mean: Large Samples 7-3 Testing a Claim about a Mean: Small Samples 7-4 Testing a Claim about a Proportion 7- 5 Testing a Claim about a Standard     Deviation (will cover with chap 8) Chapter 7Hypothesis Testing

7-1 Basics of Hypothesis Testing

Hypothesis in statistics, is a statement regarding a characteristic of one or more populations Definition

Statement is made about the population Evidence in collected to test the statement Data is analyzed to assess the plausibility of the statement Steps in Hypothesis Testing

Components of aFormal Hypothesis Test

Form Hypothesis Calculate Test Statistic Choose Significance Level Find Critical Value(s) Conclusion Components of a Hypothesis Test

A hypothesis set up to be nullified or refuted in order to support an alternate hypothesis. When used, the null hypothesis is presumed true until statistical evidence in the form of a hypothesis test indicates otherwise. Null Hypothesis: H0

Statement about value of population parameter like m, p or s Must contain condition of equality =, , or Test the Null Hypothesis directly RejectH0 or fail to rejectH0 Null Hypothesis: H0

Must be true if H0 is false , <, > ‘opposite’ of Null sometimes used instead of Alternative Hypothesis: H1 H1 Ha

If you are conducting a study and want to use a hypothesis test to support your claim, the claim must be worded so that it becomes the alternative hypothesis. The null hypothesis must contain the condition of equality Note about Forming Your Own Claims (Hypotheses)

Set up the null and alternative hypothesis The packaging on a lightbulb states that the bulb will last 500 hours. A consumer advocate would like to know if the mean lifetime of a bulb is different than 500 hours. A drug to lower blood pressure advertises that it drops blood pressure by 20%. A doctor that prescribes this medication believes that it is less. Set up the null and alternative hypothesis. (see hw # 1) Examples

a value computed from the sample data that is used in making the decision about the rejection of the null hypothesis Testing claims about the population proportion Test Statistic x - µ σ Z*= n

Critical Region - Set of all values of the test statistic that would cause a rejection of the null hypothesis Critical Value - Value or values that separate the critical region from the values of the test statistics that do not lead to a rejection of the null hypothesis

One Tailed Test Critical Region and Critical Value Critical Region Critical Value ( z score )

Two Tailed Test Critical Region and Critical Value Critical Regions Critical Value ( z score ) Critical Value ( z score )

Denoted by  The probability that the test statistic will fall in the critical region when the null hypothesis is actually true. Common choices are 0.05, 0.01, and 0.10 Significance Level

Two-tailed,Right-tailed,Left-tailed Tests The tails in a distribution are the extreme regions bounded by critical values.

H0: µ = 100 H1: µ  100 Two-tailed Test  is divided equally between the two tails of the critical region Means less than or greater than Reject H0 Fail to reject H0 Reject H0 100 Values that differ significantly from 100

H0: µ  100 H1: µ > 100 Fail to reject H0 Reject H0 Right-tailed Test Points Right Values that differ significantly from 100 100

H0: µ  100 H1: µ < 100 Left-tailed Test Points Left Reject H0 Fail to reject H0 Values that differ significantly from 100 100

Traditional Method Reject H0if the test statistic falls in the critical region Fail to reject H0if the test statistic does not fall in the critical region P-Value Method Reject H0if the P-value is less than or equal  Fail to reject H0if the P-value is greater than the  Conclusions in Hypothesis Testing

Finds the probability (P-value) of getting a result and rejects the null hypothesis if that probability is very low Uses test statistic to find the probability. Method used by most computer programs and calculators. Will prefer that you use the traditional method on HW and Tests P-Value Methodof Testing Hypotheses

Two tailed test p(z>a) + p(z<-a) One tailed test (right) p(z>a) One tailed test (left) p(z<-a) Finding P-values Where “a” is the value of the calculated test statistic Used for HW # 3 – 5 – see example on next two slides

Determine P-value Sample data: x = 105 or z* = 2.66 Reject H0: µ = 100 Fail to Reject H0: µ = 100 * µ = 73.4 or z = 0 z = 1.96 z* = 2.66 Just find p(z > 2.66)

Determine P-value Sample data: x = 105 or z* = 2.66 Reject H0: µ = 100 Reject H0: µ = 100 Fail to Reject H0: µ = 100 * z = - 1.96 µ = 73.4 or z = 0 z = 1.96 z* = 2.66 Just find p(z > 2.66) + p(z < -2.66)

Always test the null hypothesis Choose one of two possible conclusions 1. Reject the H0 2. Fail to reject the H0 Conclusions in Hypothesis Testing

Never “accept the null hypothesis, we will fail to reject it. Will discuss this in more detail in a moment We are not proving the null hypothesis Sample evidence is not strong enough to warrant rejection (such as not enough evidence to convict a suspect – guilty vs. not guilty) Accept versus Fail to Reject

Accept versus Fail to Reject

Need to formulate correct wording of finalconclusion Conclusions in Hypothesis Testing

Wording of final conclusion 1. Reject the H0 Conclusion: There is sufficient evidence to conclude………………………(what ever H1 says) 2. Fail to reject the H0 Conclusion: There is not sufficient evidence to conclude ……………………(what ever H1 says) Conclusions in Hypothesis Testing

State a conclusion The proportion of college graduates how smoke is less than 27%. Reject Ho: The mean weights of men at FLC is different from 180 lbs. Fail to Reject Ho: Example Used for #6 on HW

The mistake of rejecting the null hypothesis when it is true. (alpha) is used to represent the probability of a type I error Example: Rejecting a claim that the mean body temperature is 98.6 degrees when the mean really does equal 98.6 (test question) Type I Error

the mistake of failing to reject the null hypothesis when it is false. ß (beta) is used to represent the probability of a type II error Example: Failing to reject the claim that the mean body temperature is 98.6 degrees when the mean is really different from 98.6 (test question) Type II Error

Type I and Type II Errors True State of Nature H0 True H0 False Reject H0 Correct decision Type I error  Decision Fail to Reject H0 Type II error  Correct decision In this class we will focus on controlling a Type I error. However, you will have one question on the exam asking you to differentiate between the two.

a = p(rejecting a true null hypothesis) b = p(failing to reject a false null hypothesis) n, a and b are all related Type I and Type II Errors

Identify the type I and type II error. The mean IQ of statistics teachers is greater than 120. Type I: We reject the mean IQ of statistics teachers is 120 when it really is 120. Type II: We fail to reject the mean IQ of statistics teachers is 120 when it really isn’t 120. Example

For any fixed sample size n, as  decreases,  increases and conversely. To decrease both  and , increase the sample size. Controlling Type I and Type II Errors

Power of a Hypothesis Test is the probability (1 - ) of rejecting a false null hypothesis. Note: No exam questions on this. Usually covered in a more advanced class in statistics. Definition

7-2 Testing a claim about the mean (large samples)

Goal Identify a sample result that is significantly different from the claimed value By Comparing the test statistic to the critical value Traditional (or Classical) Method of Testing Hypotheses

Determine H0 and H1. (and if necessary) Determine the correct test statistic and calculate. Determine the critical values, the critical region and sketch a graph. Determine Reject H0 or Fail to reject H0 State your conclusion in simple non technical terms. Traditional (or Classical) Method of Testing Hypotheses (MAKE SURE THIS IS IN YOUR NOTES)

Test Statistic for Testing a Claim about a Proportion Can Use Traditional method Or P-value method

1) Traditional method 2) P-value method 3) Confidence intervals Three Methods Discussed

for testing claims about population means 1) The sample is a random sample. 2) The sample is large (n > 30). a) Central limit theorem applies b) Can use normal distribution 3) If  is unknown, we can use sample standard deviation s as estimate for . Assumptions

Test Statistic for Claims about µ when n > 30 x - µx Z*=  n

Reject the null hypothesis if the test statistic is in the critical region Fail to reject the null hypothesis if the test statistic is not in the critical region Decision Criterion

Claim:  = 69.5 years H0 :  = 69.5 H1 :  69.5 Example:A newspaper article noted that the mean life span for 35 male symphony conductors was 73.4 years, in contrast to the mean of 69.5 years for males in the general population. Test the claim that there is a difference. Assume a standard deviation of 8.7 years. Choose your own significance level. Step 1: Set up Claim, H0, H1 Select if necessary level:  = 0.05

Step 2: Identify the test statistic and calculate x - µ 73.4 – 69.5 z*=== 2.65  8.7 n 35

Step 3: Determine critical region(s) and critical value(s) & Sketch = 0.05 /2= 0.025 (two tailed test) 0.4750 0.4750 0.025 0.025 z = - 1.96 1.96 Critical Values - Calculator

• More by User

Chapter 8 Hypothesis Testing

Chapter 8 Hypothesis Testing. 8.2 Basics of Hypothesis Testing 8.3 Testing about a Proportion p 8.4 Testing about a Mean µ ( σ known ) 8.5 Testing about a Mean µ ( σ unknown ) 8.6 Testing about a Standard Deviation σ. Section 8.2 Basics of Hypothesis Testing. Objective

1.21k views • 65 slides

Chapter 9 Hypothesis Testing

Chapter 9 Hypothesis Testing. 9.1 The Language of Hypothesis Testing. Example: Illustrating Hypothesis Testing.

939 views • 32 slides

Chapter 9 Hypothesis Testing. 9.1 The Language of Hypothesis Testing. Steps in Hypothesis Testing 1. A claim is made. Steps in Hypothesis Testing 1. A claim is made. 2. Evidence (sample data) is collected in order to test the claim. Steps in Hypothesis Testing 1. A claim is made.

677 views • 30 slides

Chapter 9 Hypothesis Testing. Developing Null and Alternative Hypotheses Type I and Type II Errors One-Tailed Tests About a Population Mean: Large-Sample Case Two-Tailed Tests About a Population Mean: Large-Sample Case Tests About a Population Mean: Small-Sample Case

658 views • 43 slides

Chapter 9 Hypothesis Testing. Testing Hypothesis about µ, when the s.t of population is known. THREE WAYS TO STRUCTURE THE HYPOTHESIS TEST:.

526 views • 36 slides

Chapter 10: Hypothesis Testing

Chapter 10: Hypothesis Testing. Outline (Topics from 10.2 and 10.4). Hypothesis Testing Definitions The p value Examples and summary of steps Significance levels. Z-test for means and proportions. Tests of significance. How do we determine how good our estimate of s parameter is?

1.22k views • 36 slides

Chapter 8 Hypothesis Testing. Definitions 1 Sample Mean Z-test 1 Sample Mean T-test 1 Proportion Z-test 2 Independent Samples T-test 2 Related Samples Paired Data Type of Errors. Definition. Hypotheses Test Statistic P-value Decision and Conclusion. Definition.

685 views • 34 slides

Chapter 9 Hypothesis Testing. Developing Null and Alternative Hypotheses. Type I and Type II Errors. Population Mean: s Known. Population Mean: s Unknown. Developing Null and Alternative Hypotheses. Hypothesis testing can be used to determine whether

490 views • 21 slides

Chapter 8 Hypothesis Testing. 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean: Known 8-5 Testing a Claim About a Mean: Not Known 8-6 Testing a Claim About a Standard Deviation or Variance.

2.14k views • 103 slides

Chapter 6 Hypothesis Testing

Chapter 6 Hypothesis Testing. Standard Deviation. Regression. Dependent variable. Independent variable (x). Regression is the attempt to explain the variation in a dependent variable using the variation in independent variables. Regression is thus an explanation of causation.

826 views • 65 slides

Chapter 7: Hypothesis Testing

Chapter 7: Hypothesis Testing. A hypothesis is a conjecture about a population. Typically, these hypotheses will be stated in terms of a parameter. A test of hypothesis is a statistical procedure used to make a decision about the conjectured value of a parameter.

1.05k views • 31 slides

Chapter 10 – Hypothesis Testing

Chapter 10 – Hypothesis Testing. What is a hypothesis? A statement about a population that may or may not be true. What is hypothesis testing? A statistical test to prove or disprove a hypothesis. At the end of the test, either the hypothesis is rejected or not rejected.

662 views • 31 slides

Chapter 9 Hypothesis Testing. Introduction to Statistical Tests Testing the Mean µ Testing a Proportion p Tests Involving Paired Differences Testing µ1-µ2 and p1-p2. 9.1 Introduction to Statistical Tests. We can draw inference on a population parameter in two ways: Estimation (Chapter 8)

986 views • 78 slides

Chapter 8 Hypothesis Testing. 8-1 Review and Preview 8-2 Basics of Hypothesis Testing 8-3 Testing a Claim about a Proportion 8-4 Testing a Claim About a Mean: σ Known 8-5 Testing a Claim About a Mean: σ Not Known 8-6 Testing a Claim About a Standard Deviation or Variance.

1.97k views • 144 slides

Chapter 9 -Hypothesis Testing

Chapter 9 -Hypothesis Testing. Hypothesis testing can be used to determine whether a statement about the value of a population parameter should or should not be rejected. The null hypothesis , denoted by H 0 , is a tentative assumption about a population parameter.

556 views • 40 slides

1.22k views • 71 slides

chapter-7 hypothesis testing for quantitative variable

chapter-7 hypothesis testing for quantitative variable. contents. introduction Hypothesis testing 2.1 One sample t test 2.2 two independent-samples t test 2.3 Paired-samples t test. Hypothesis testing.

826 views • 82 slides

Chapter 6 Hypothesis Testing. What is Hypothesis Testing?. … the use of statistical procedures to answer research questions Typical research question (generic): For hypothesis testing, research questions are statements: This is the null hypothesis (assumption of “no difference”)

579 views • 57 slides

Chapter 9 Hypothesis Testing. Chapter Outline. Developing Null and Alternative Hypothesis Type I and Type II Errors Population Mean: Known Population Mean: Unknown Population Proportion. Hypothesis Testing.

652 views • 46 slides

Chapter 9 : Hypothesis Testing

Chapter 9 : Hypothesis Testing. Section 7 : Testing Differences of Two Means or Two Proportions (Independent Samples). Large Samples (Independent). Test Statistic. Test Statistic. , as stated in the null hypothesis

125 views • 9 slides

Chapter Seventeen HYPOTHESIS TESTING

Chapter Seventeen HYPOTHESIS TESTING. Approaches to Hypothesis Testing. Classical Statistics vs. Bayesian Approach Classical Statistics sampling-theory approach Making inference about a population based on sample evidence objective view of probability

152 views • 14 slides