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 ofp-values.
-Consider the scenario:
– 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.

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.

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-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.”