There are two types of errors in hypothesis testing: Type I and Type II errors. A Type I error occurs when the null hypothesis is rejected when it is actually true. A Type II error occurs when the null hypothesis is not rejected even when it is false. To minimize Type I errors, the significance level should be decreased, while to minimize Type II errors, the significance level should be increased or the sample size improved. The tradeoff is that minimizing one error type tends to increase the other, so the costs of each error must be considered.