Logic of Hypothesis Testing

Logic of Hypothesis Testing

In Week 4, you explored how the confidence interval helps to estimate a population mean. Hypothesis testing is an approach that allows us to make some determination about whether a hypothesis should be rejected, based upon sample statistics. This approach is integral to the scientific method and provides us with a measureable level of certainty when making inferences back to the population.

Consider the following:

A researcher is conducting work on social inequality and wants to know whether there are marked differences between socioeconomic status of Caucasians and Non-Caucasians. Since the researcher cannot measure the entire population, a sample is drawn and a hypothesis can be constructed and evaluated as to whether any noticeable differences in the sample also likely appear in the population.

As a scholar-practitioner, it will be important for you to develop your knowledge and skillset in hypothesis testing. As evident in the scenario provided, hypothesis testing establishes a process to determine the probability of observing similar scores noted in the sample under the null hypothesis.

For this week, you will examine hypothesis testing and determine the statistical significance and meaningfulness in the data. You also will explore the results of data to determine implications for social change.

Learning Objectives

Students will:

· Evaluate statements related to null hypothesis

· Evaluate p-values

· Evaluate type I and type II errors

· Evaluate for meaningfulness

· Evaluate statistical significance

· Evaluate sample size

· Analyze implications for social change

Learning Resources

Required Readings

Frankfort-Nachmias, C., & Leon-Guerrero, A. (2018). Social statistics for a diverse society (8th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 8, “Testing Hypothesis” (pp. 203-204)

Wagner, W. E. (2016). Using IBM® SPSS® statistics for research methods and social science statistics (6th ed.). Thousand Oaks, CA: Sage Publications.

· Chapter 6, “Testing Hypotheses Using Means and Cross-Tabulation”

Warner, R. M. (2012). Applied statistics from bivariate through multivariate techniques (2nd ed.). Thousand Oaks, CA: Sage Publications.

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center.

· Chapter 3, “Statistical Significance Testing” (pp. 81–124)

Applied Statistics From Bivariate Through Multivariate Techniques, 2nd Edition by Warner, R.M. Copyright 2012 by Sage College. Reprinted by permission of Sage College via the Copyright Clearance Center. 

Magnusson, K. (n.d.). Welcome to Kristoffer Magnusson’s blog about R, Statistics, Psychology, Open Science, Data Visualization [blog]. Retrieved from http://rpsychologist.com/index.html

As you review this web blog, select [Updated] Statistical Power and Significance Testing Visualization link, once you select the link, follow the instructions to view the interactive for statistical power. This interactive website will help you to visualize and understand statistical power and significance testing.

Note: This is Kristoffer Magnusson’s personal blog and his views may not necessarily reflect the views of Walden University faculty.

American Statistical Association (2016). American Statistical Association Releases Statement on Statistical Significance and P-Values. Retrieved from http://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf

As you review this press release, consider the misconceptions and the misuse of p-values in quantitative research.

Document: Week 5 Scenarios (PDF)

Use these scenarios to complete this week’s Assignment.

Datasets

Document: Data Set 2014 General Social Survey (dataset file)

Use this dataset to complete this week’s Discussion.

Note: You will need the SPSS software to open this dataset.

Document: Data Set Afrobarometer (dataset file)

Use this dataset to complete this week’s Assignment.

Note: You will need the SPSS software to open this dataset.

Document: High School Longitudinal Study 2009 Dataset (dataset file)

Use this dataset to complete this week’s Assignment.

Note: You will need the SPSS software to open this dataset.

Required Media

Laureate Education (Producer). (2016f). Meaningfulness vs. statistical significance [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 4 minutes.

In this media program, Dr. Matt Jones discusses the differences in meaningfulness and statistical significance. Focus on how this information will inform your Discussion and Assignment for this week.

Accessible player 

Laureate Education (Producer). (2016n). Halfway point [Video file]. Baltimore, MD: Author.

Note: The approximate length of this media piece is 2 minutes.

In this media program, Dr. Annie Pezalla, Associate Director of Curriculum and Assessment with the Center for Research Quality at Walden University, discusses what you have learned so far in the course. She also discusses what you have to look forward to as well as things to look out for in the remainder of the course.

Accessible player 

Optional Resources

Skill Builders:

· Evaluating P Values

· Statistical Power

To access these Skill Builders, navigate back to your Blackboard Course Home page, and locate “Skill Builders” in the left navigation pane. From there, click on the relevant Skill Builder link for this week.

You are encouraged to click through these and all Skill Builders to gain additional practice with these concepts. Doing so will bolster your knowledge of the concepts you’re learning this week and throughout the course.

Discussion: Statistical Significance and Meaningfulness

Once you start to understand how exciting the world of statistics can be, it is tempting to fall into the trap of chasing statistical significance. That is, you may be tempted always to look for relationships that are statistically significant and believe they are valuable solely because of their significance. Although statistical hypothesis testing does help you evaluate claims, it is important to understand the limitations of statistical significance and to interpret the results within the context of the research and its pragmatic, “real world” application.

As a scholar-practitioner, it is important for you to understand that just because a hypothesis test indicates a relationship exists between an intervention and an outcome, there is a difference between groups, or there is a correlation between two constructs, it does not always provide a default measure for its importance. Although relationships are significant, they can be very minute relationships, very small differences, or very weak correlations. In the end, we need to ask whether the relationships or differences observed are large enough that we should make some practical change in policy or practice.

For this Discussion, you will explore statistical significance and meaningfulness.

To prepare for this Discussion:

· Review the Learning Resources related to hypothesis testing, meaningfulness, and statistical significance.

· Review Magnusson’s web blog found in the Learning Resources to further your visualization and understanding of statistical power and significance testing.

· Review the American Statistical Association’s press release and consider the misconceptions and misuse of p-values.

· Consider the scenario:

· A research paper claims a meaningful contribution to the literature based on finding statistically significant relationships between predictor and response variables. In the footnotes, you see the following statement, “given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level.”

Post your response to the scenario in which you critically evaluate this footnote. As a reader/reviewer, what response would you provide to the authors about this footnote?

Be sure to support your Main Post and Response Post with reference to the week’s Learning Resources and other scholarly evidence in APA Style.

Assignment: Evaluating Significance of Findings

Part of your task as a scholar-practitioner is to act as a critical consumer of research and ask informed questions of published material. Sometimes, claims are made that do not match the results of the analysis. Unfortunately, this is why statistics is sometimes unfairly associated with telling lies. These misalignments might not be solely attributable to statistical nonsense, but also “user error.” One of the greatest areas of user error is within the practice of hypothesis testing and interpreting statistical significance. As you continue to consume research, be sure and read everything with a critical eye and call out statements that do not match the results.

For this Assignment, you will examine statistical significance and meaningfulness based on sample statements.

To prepare for this Assignment:

· Review the Week 5 Scenarios found in this week’s Learning Resources and select two of the four scenarios for this Assignment.

· For additional support, review the Skill Builder: Evaluating P Values and the Skill Builder: Statistical Power, which you can find by navigating back to your Blackboard Course Home Page. From there, locate the Skill Builder link in the left navigation pane.

For this Assignment:

Critically evaluate the two scenarios you selected based upon the following points:

· Critically evaluate the sample size.

· Critically evaluate the statements for meaningfulness.

· Critically evaluate the statements for statistical significance.

· Based on your evaluation, provide an explanation of the implications for social change.

Use proper APA format and citations, and referencing.

Submit your Evaluating Significance of Findings Assignment.

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