A Comment on “Beyond P-value: the Rigor and Power of Study"

Author(s):  
Helena Kraemer

“As ye sow. So shall ye reap”: For almost 100 years, researchers have been taught that the be-all and end-all in data-based research is the p-value. The resulting problems have now generated concern, often from us who have long so taught researchers. We must bear a major responsibility for the present situation and must alter our teachings. Despite the fact that the Zhang and Hughes paper is titled “Beyond p-value”, the total focus remains on statistical hypothesis testing studies (HTS) and p-values(1). Instead, I would propose that there are three distinct, necessary, and important phases of research: 1) Hypothesis Generation Studies (HGS) or Exploratory Research (2-4); 2) Hypothesis Testing Studies (HTS); 3) Replication and Application of Results. Of these, HTS is undoubtedly the most important, but without HGS, HTS is often weak and wasteful, and without Replication and Application, the results of HTS are often misleading.

2019 ◽  
Vol 81 (8) ◽  
pp. 535-542
Author(s):  
Robert A. Cooper

Statistical methods are indispensable to the practice of science. But statistical hypothesis testing can seem daunting, with P-values, null hypotheses, and the concept of statistical significance. This article explains the concepts associated with statistical hypothesis testing using the story of “the lady tasting tea,” then walks the reader through an application of the independent-samples t-test using data from Peter and Rosemary Grant's investigations of Darwin's finches. Understanding how scientists use statistics is an important component of scientific literacy, and students should have opportunities to use statistical methods like this in their science classes.


2015 ◽  
Vol 14 (2) ◽  
pp. 7-27
Author(s):  
BIRGIT C. AQUILONIUS ◽  
MARY E. BRENNER

Results from a study of 16 community college students are presented. The research question concerned how students reasoned about p-values. Students' approach to p-values in hypothesis testing was procedural. Students viewed p-values as something that one compares to alpha values in order to arrive at an answer and did not attach much meaning to p-values as an independent concept. Therefore it is not surprising that students often were puzzled over how to translate their statistical answer to an answer of the question asked in the problem. Some reflections on how instruction in statistical hypothesis testing can be improved are given. First published November 2015 at Statistics Education Research Journal Archives


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