In statistics, alpha is also known as the level of significance. It represents the probability of obtaining your results due to chance. The smaller this value is, the more "unusual" the results, indicating that the sample is from a different population than its being compared to. Alpha also represents your chance of making a Type I Error, which is the chance that you reject the null hypothesis when in reality, you should fail to reject the null hypothesis. Alpha levels are used in hypothesis tests, and usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10. Alpha levels can be controlled by you and are related to confidence levels. To get alpha, subtract your confidence level from 1. For example, if you want to be 95 percent confident that your analysis is correct, the alpha level would be 1 - .95 = 5 percent, assuming you had a one-tailed test. For two-tailed tests, divide the alpha level by 2.