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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.
 

Required Readings

Frankfort-Nachmias, C., Leon-Guerrero, A., & Davis, G. (2020). Social statistics for a diverse society (9th ed.). Thousand Oaks, CA: Sage Publications.
Chapter 8, “Testing Hypothesis: Assumptions of Statistical Hypothesis Testing” (pp. 241-242)

Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th 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.