Related Experiment Video
Updated: Aug 14, 2026
06:52
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
Published on: May 26, 2020
Beyond the p -value: Pitfalls in Reporting and Applying Statistical Tests in Sports Science
Roberto C M Leite1, Rhaí A Arriel1, Rodney Paixão1
1Integrated Laboratory of Physiology and Performance (LABIFID)Department of Biophysics and PhysiologyFederal University of Juiz de ForaJuiz de ForaMinas GeraisBrazil.
Sports Medicine International Open
|August 13, 2026
Summary
Most sports science research (87.4%) has statistical reporting or analysis flaws, impacting validity and interpretation. Improving statistical literacy and peer review is crucial for rigorous sports science.
Area of Science:
- Sports Science
- Statistics
- Research Methodology
Background:
- Statistical inference is vital for sports science research credibility.
- Concerns exist regarding the rigor of statistical reporting and analysis in the field.
- Established methodological guidelines exist for statistical practices.
Purpose of the Study:
- To assess the adequacy of statistical practices in sports science research.
- To identify common reporting omissions and statistical misapplications.
- To evaluate the relationship between statistical adequacy and journal ranking.
Main Methods:
- Systematic review of 167 original studies from 2020.
- Studies were selected from 15 sports science journals (Q1-Q3 JCR indexed).
- Data on study characteristics and statistical methods were extracted and analyzed.
Main Results:
- 87.4% of studies exhibited at least one statistical inadequacy.
- Frequent omissions included failure to report statistical assumptions (normality, homogeneity of variances, sphericity).
- Common misapplications involved inappropriate dependent variables and treating independent observations as dependent.
Conclusions:
- Significant statistical deficiencies exist across sports science research, regardless of journal quartile.
- These issues threaten research validity, reproducibility, and practical interpretation.
- Enhanced statistical literacy, stricter reporting standards, and improved peer review are necessary.
Related Concept Videos
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:00Detection of Gross Error: The Q Test
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
01:16Statistical Hypothesis Testing
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
01:16Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
01:14Errors In Hypothesis Tests
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
01:09Decision Making: P-value Method
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
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 have a...
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 have a...