Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

1 results

  • 6 - Is Bigger Always Better?

  • from Part II - Rethinking Research
  • Karen B. Schmaling, Washington State University, Robert M. Kaplan, Stanford University
  • Book:
    Rethinking Clinical Research
    Published online:
    13 March 2025
    Print publication:
    20 March 2025, pp 117-136

  • Summary

    The criteria for evaluating research studies often include large sample size. It is assumed that studies with large sample sizes are more meaningful than those that include a fewer number of participants. This chapter explores biases associated with the traditional application of null hypothesis testing. Statisticians now challenge the idea that retention of the null hypothesis signifies that a treatment is not effective. A finding associated with an exact probability value of p = 0.049 is not meaningfully different from one in which p = 0.051. Yet the interpretation of these two studies can be dramatically different, including the likelihood of publication. Large studies are not necessarily more accurate or less biased. In fact, biases in sampling strategy are amplified in studies with large sample sizes. These problems are of increasing concern in the era of big data and the analysis of electronic health records. Studies that are overpowered (because of very large sample sizes) are capable of identifying statistically significant differences that are of no clinical importance.