# 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 relat

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.

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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:

o 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.”

By Day 3

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.

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)

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.