Outcomes to an experiment or repeated events are statistically significant if they differ from what chance variation might produce. For example – suppose n people are given a medication. If their response to the medication lies outside the range of how samples of size n might respond when not given the medication, the response is statistically significant. The philosophical basis for significance testing lies in the fact that random variation pervades all aspects of life, and in the desire to avoid being fooled by what might be chance variation. Significance testing involves the evaluation of two opposing hypotheses. The “alternative hypothesis” typically describes some change or effect that you expect or hope to see confirmed by data. For example, new drug A works better than standard drug B. Or the accuracy of a new weapon targeting system is better than historical standards. The “null hypothesis” embodies the presumption that nothing has changed, or that there is no difference.