Alternatives to the statistical mass confusion of testing for no-effect

TL;DR

This paper critiques the traditional null hypothesis significance testing in cell biology, emphasizing the importance of effect size interpretation to improve accuracy and understanding in high-throughput experiments.

Contribution

It proposes shifting from null hypothesis testing to effect size-based analysis for more reliable and meaningful results in cell biology research.

Findings

Null hypothesis testing is often misinterpreted and error-prone.

Effect size interpretation provides clearer insights into biological effects.

A new approach improves reliability in high-throughput cell biology experiments.

Abstract

In cell biology, statistical analysis means testing the hypothesis that there was no effect. This weak form of hypothesis testing neglects effect size, is universally misinterpreted, and is disastrously prone to error when combined with high-throughput cell biology. The solution is for analysis of measurements to start and end with an interpretation of effect size.