AI Article Synopsis
- Many social scientists tend to overestimate the trustworthiness of results from small-sample studies, which can lead to misleading conclusions.
- The rise of "user-friendly" statistics software can worsen this issue by enabling quick exploration of data, making it easier to misinterpret findings as reliable.
- The authors argue that statistically significant results from small samples (like n = 30) may not be trustworthy and advocate for multiple replications to confirm findings.
Article Abstract
Many social scientists appear to possess an overconfidence in the reliability of research results from a single, small-sample, inferential study. In this article, the authors speculate that "user-friendly" statistics packages have the potential to exacerbate statistical misinterpretation by providing researchers with a tool to explore data easily and identify what is interpreted as "reliable" relationships. This article contains an empirical demonstration of the potential problems that arise when a large number of statistical tests are interpreted. Results show that statistically significant results may be unreliable. Also, a zero relationship can erroneously appear as a medium to large effect size relationship when a small sample is used (e.g., n = 30). The authors suggest the need for multiple replications as the criterion of a reliable finding.
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| http://dx.doi.org/10.1080/00223980009600857 | DOI Listing |
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