2. Why Sample Size is Important?

2.1. Components of Sample Size Calculations

greater the variability in the outcome variable (e.g blood pressure) across study population, the larger the sample size required to assess whether an observed effect is a true effect. On the other hand, the more effective (or harmful!) a tested treatment is, the smaller the sample size needed to detect this positive or negative effect. Calculating the sample size for a trial requires five basic components:

Summary of the components for sample size calculations

Component

Definition

Alpha (α) (Type I error)

The probability of falsely rejecting the null hypothesis (H0) and detecting a statistically significant difference when the groups in reality are not different, i.e. the chance of a false-positive result.

Beta (β) (Type II error)

The probability of falsely accepting H0 and not detecting a statistically significant difference when a specified difference between the groups exists in reality, i.e. the chance of a false-negative result.

Power (1-β)

The probability of correctly rejecting H0 and detecting a statistically significant difference when a specified difference between the groups in reality exists.

Minimal clinically relevant difference

The minimal difference between the groups that the investigator considers biologically plausible and clinically relevant.

Variance

The variability of the outcome measure, expressed as the Standard Deviation (SD) in case of a continuous outcome.

Abbreviations: H0 – null hypothesis; the null hypothesis states that compared groups are not different from each other). SD – standard deviation.