The non-significance factor is a simple posterior estimate of the minimum necessary sample size

TL;DR

This paper introduces a straightforward posterior estimate method to determine the minimum sample size needed for achieving significance in a test, based on previous non-significant results.

Contribution

It presents a novel, simple approach to estimate the necessary sample size for significance using posterior estimation, addressing a common research challenge.

Findings

Provides a practical method for sample size estimation after non-significant results

Simplifies the process of planning future studies based on prior data

Offers a Bayesian perspective on significance testing

Abstract

A researcher is interested in what sample size is needed to get the required significance of the same test, assuming exactly the same situation that was in the study with the non-significant result. We propose a simple solution to the problem.