# Small Samples, Big Problems, Statistical Tests in Nematology Research Need Power

## TL;DR

The paper highlights problems with small sample sizes and poor statistical reporting in nematology research and suggests using power analyses to improve reliability.

## Contribution

The novelty lies in recommending a priori power analyses and clear statistical reporting to enhance validity and reproducibility in nematology studies.

## Key findings

- Recent nematology publications often lack justified sample sizes and clear statistical reporting.
- Conducting a priori power analyses can estimate adequate sample sizes for reliable results.
- Reporting effect sizes and descriptive statistics strengthens research reliability.

## Abstract

In nematology research, hypothesis testing is a fundamental method and is typically supported by statistical significance (e.g., P-value <0.05). However, our review of recent publications in nematology reveals frequent issues, including unjustified sample size and unclear reporting of statistical methods, which undermines the validity and reproducibility of the results. To address these issues, we recommend researchers to conduct a priori power analyses to estimate adequate sample sizes and report key descriptive statistics (e.g., effect size). These practices not only strengthen the reliability of research, but can also help answer a central question for investigators: How many samples are needed to detect a “truly” statistically significant difference in an experiment?