What are some best practices for reporting sample size and power estimates in your scientific papers?

1 Why report sample size and power?

Reporting your sample size and power estimates is not only a good practice, but also a requirement for many journals and funders. It shows that you have planned your study carefully, that you have considered the feasibility and validity of your results, and that you have minimized the risk of false positives or negatives. It also helps your readers and reviewers to assess the quality and reliability of your evidence, and to compare it with other studies in your field.

2 How to calculate sample size and power?

There are different methods and tools for calculating sample size and power, depending on your research question, hypothesis, and statistical test. Generally, you need to specify the following parameters: the effect size, the significance level, the power, and the variance or standard deviation. The effect size is the difference or association that you expect to find between your groups or variables. The significance level is the probability of rejecting the null hypothesis when it is true, usually set at 0.05. The power is the probability of detecting the effect size when it is present, usually set at 0.8 or higher. The variance or standard deviation is the measure of variability or dispersion of your data. You can use online calculators, software packages, or formulas to estimate your sample size and power, based on these parameters.

3 What to report in your paper?

When you report your sample size and power estimates in your paper, you should include the following information: the rationale and assumptions behind your calculations, the method and source that you used, the parameters and values that you entered, and the results and implications that you obtained. You should also report any adjustments or corrections that you made for factors such as multiple comparisons, attrition, clustering, or covariates. You should present your sample size and power estimates in a clear and concise way, preferably in a table or a figure, and refer to them in your methods and results sections.

4 What are some common errors and limitations?

Reporting your sample size and power estimates is not a guarantee of validity or reproducibility, as there are some common errors and limitations that you should be aware of and avoid. Some of these are: using inappropriate or outdated methods or sources, relying on arbitrary or unrealistic parameters or values, ignoring or misinterpreting the uncertainty or variability of your estimates, failing to account for the design or analysis features of your study, and overinterpreting or misrepresenting your results. You should always check the accuracy and suitability of your calculations, and acknowledge the limitations and assumptions of your estimates.

5 How to improve your reporting skills?

Reporting your sample size and power estimates is a skill that you can improve with practice and feedback. You can use some of the following resources and strategies to enhance your reporting skills: consult the guidelines and standards of your field or journal, review the examples and critiques of other papers in your area, seek advice from experts or colleagues with more experience, use validated and updated tools or software, and revise and edit your reports carefully.