Related Experiment Video
Updated: Dec 24, 2025
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
Published on: August 16, 2017
Redefining significance and reproducibility for medical research: A plea for higher P-value thresholds for diagnostic
Ewout W Steyerberg1, Ben Van Calster1,2
1Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
Abstract:
The role of P-values for null hypothesis testing is under debate. We aim to explore the impact of the significance threshold on estimates for the strengths of associations ("effects") and the implications for different types of epidemiological research. We consider situations with normal distribution of a true effect, while varying the effect size. We confirm the occurrence of "testimation bias": estimating effect size only if the test was statistically significant leads to exaggerated results. The absolute bias is largest for true effects around 0.7 times the size of the standard error: +220% bias if effects are selected after testing with P < .05, and +335% if tested with P < .005. Less bias was found for testing with P < .20 (+130%) and larger true effect sizes. We conclude that a lower P-value threshold for declaring statistical significance implies more exaggeration in an estimated effect. This implies that if a low threshold is used, effect size estimation should not be attempted, for example in the context of selecting promising discoveries that need further validation. Confirmatory studies, such as randomized controlled trials, might stick to the 0.05 threshold if adequately powered, while prediction modelling studies should use an even higher threshold, such as 0.2, to avoid strongly biased effect estimates.
Related Concept Videos
01:09Decision Making: P-value Method
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
01:13Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
01:10P-value
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...
01:04Significance Testing: Overview
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:16Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...