To compute a P value, you first must clearly define a null hypothesis, usually that two means (or proportions or EC50’s…) are identical. Given some assumptions, the P values are the probability of seeing an effect as large as or larger than you observed in the current experiment if in fact the null hypothesis was true. But note that the P value gives you no information about how large the difference (or effect) is. Figure 4 demonstrates this point by plotting the P values that result from comparing two samples in experiments with different sample sizes. Even though the means and standard deviations are identical for each simulated experiment, the P values are far from identical. With n = 3 in each group, the P value is 0.65. When n = 300 in each group, the P value is 0.000001.