What is the best way to minimize experimental variability?

Increasing the sample size is a key method for minimizing experimental variability because a larger sample provides a more accurate representation of the population. This increase reduces the impact of random variation and outliers in the data, leading to more reliable and generalizable results.

When the sample size is small, individual differences among subjects can disproportionately affect the outcome, resulting in higher variability. Conversely, a larger sample allows for better statistical power, which enhances the ability to detect true effects and relationships within the data. By achieving greater consistency across a broader range of subjects, the findings become more robust and less susceptible to the influences of chance.

This method contrasts sharply with reducing the number of trials, which can actually increase variability due to insufficient data. Using a wider variety of subjects might introduce additional variability rather than minimize it, as it incorporates more potential sources of differences. Lastly, varying the experimental conditions can also lead to increased variability, as it introduces new variables that could confound the results.