(or in some fields 0.01 or 0.10). Alpha denotes the fixed significance level for a given hypothesis test. Before starting any statistical analyses, along with stating hypotheses, you choose a significance level you're testing at. This states the threshold at which you are prepared to accept the possibility of a Type I Error -- otherwise known as a false positive -- rejecting a null hypothesis that is actually true. Type what error? Most often we are concerned primarily with reducing the chance of a Type I Error over its counterpart (Type II Error -- accepting a false null hypothesis). It all depends on what the impact of either error will be. Take a pharmaceutical company testing a new drug; if the drug actually doesn't work (a true null hypothesis) then rejecting this null and asserting that the drug does work could have huge repercussions -- particularly if patients are given this drug over one that actually does work. The pharmaceutical company would be concerned primarily with reducing the likelihood of a Type I Error. Sometimes, a Type II Error could be more important. Environmental testing is one such example; if the effect of toxins on water quality is examined, and in truth the null hypothesis is false (that is, the presence of toxins does affect water quality) a Type II Error would mean accepting a false null hypothesis, and concluding there is no effect of toxins. The down-stream issues could be dire, if toxin levels are allowed to remain high