How to Statistically Show the Absence of an Effect

Author: Quertemont, Etienne

Source: Psychologica Belgica, 1 August 2011, vol. 51, no. 2, pp. 109-127(19)


Buy & download fulltext article:

The full text article is temporarily unavailable.

We apologise for the inconvenience. Please try again later.


In experimental studies, the lack of statistical significance is often interpreted as the absence of an effect. Unfortunately, such a conclusion is often a serious misinterpretation. Indeed, non-significant results are just as often the consequence of an insufficient statistical power. In order to conclude beyond reasonable doubt that there is no meaningful effect at the population level, it is necessary to use proper statistical techniques. The present article reviews three different approaches that can be used to show the absence of a meaningful effect, namely the statistical power test, the equivalence test, and the confidence interval approach. These three techniques are presented with easy to understand examples and equations are given for the case of the two-sample t-test, the paired-sample t-test, the linear regression coefficient and the correlation coefficient. Despite the popularity of the power test, we recommend using preferably the equivalence test or the confidence interval.

Document Type: Research Article

Publication date: August 1, 2011

Related content


Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content

Text size:

A | A | A | A
Share this item with others: These icons link to social bookmarking sites where readers can share and discover new web pages. print icon Print this page