What is the difference between a type 1 and a type 2 error in hypothesis testing?

To understand type 1 and 2 errors you have to first understand what p values are. A p value is the probability of finding a result. In psychology, the significance level used is p=0.05 such that if a hypothesis test gives a result of p<0.05 this suggests a result is statistically significant. This means the experiment has found an effect and the null hypothesis predicting no effect is rejected. On the other hand if a hypothesis test gives a result where p is greater than 0.05, the result is not significant and the null hypothesis should be accepted. The significance value of 0.05 means that there is only a 5% chance that this result would be found due to chance.

A type 1 error involves a researcher falsely rejecting the null hypothesis and therefore falsely suggests an experiment has found an effect when it hasn't. A type 2 error is the opposite, where a researcher falsely accepts the null hypothesis and claims that there has been no effect found in an experiment when there has been. A type 1 error occurs when the signficance level (p) is too lenient (big) such as p= 0.5. A type 2 error occurs when the significance level is too small, such as p= 0.01.