How do you calculate sample size for experiments?

3 Choose the significance level and power

The significance level, also known as alpha, is the probability of rejecting the null hypothesis when it is true. In other words, it is the risk of making a false positive error or finding an effect that does not exist. The common value for the significance level is 0.05, which means that there is a 5% chance of making a false positive error. The power, also known as beta, is the probability of rejecting the null hypothesis when it is false. In other words, it is the ability of the experiment to detect a true effect. The common value for the power is 0.8, which means that there is a 20% chance of making a false negative error or missing an effect that exists. The lower the significance level and the higher the power, the larger the sample size needed.

4 Consider the variability and error

The variability and error are factors that affect the precision and accuracy of the measurement or outcome. The variability is the natural variation or diversity in the population or sample, such as age, gender, weight, or preferences. The error is the random or systematic deviation from the true value, such as measurement error, sampling error, or human error. The higher the variability and error, the larger the sample size needed to reduce them and increase the confidence in the results.

5 Use a formula or a calculator

The final step to calculate sample size is to use a formula or a calculator that applies to your type of experiment and design. There are different formulas and calculators for different scenarios, such as comparing means, proportions, correlations, or rates. You can find them online or in textbooks or software. You will need to input the parameters that you have estimated or chosen in the previous steps, such as the effect size, the significance level, the power, and the variability or error. The formula or calculator will then output the sample size required for your experiment.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?