A common misconception in experimental research is that simply increasing the sample size will solve most problems and lead to reliable results. However, if your experiment is poorly designed, no amount of data can compensate for fundamental flaws. When you overlook key factors like randomization, control groups, or confounding variables, your results may be biased or invalid, regardless of how many observations you collect. For instance, failing to randomize assignment to treatment groups can introduce systematic differences that skew your findings. Similarly, neglecting to control for confounders can make it impossible to attribute observed effects to your intervention. In these cases, adding more participants only amplifies the underlying issues, making false conclusions seem more convincing. Careful experimental design ensures that your results are credible, interpretable, and actionable — while poor design can render even the largest datasets useless.

To further illustrate the difference, consider the following table comparing the outcomes of well-designed and poorly designed experiments:

This table shows that a well-designed experiment, even with a small sample, can produce valid results efficiently. In contrast, a poorly designed experiment wastes resources and delivers unreliable findings, no matter how many participants are involved.