Type I Error vs P-Value
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Type I error
Hypothesis testing
A Type I error is rejecting a true null hypothesis: a false positive, concluding there is an effect when in fact there is none.
A Type I error is a false alarm, the mistake of finding an effect that is not really there. Its long-run probability equals the significance level you choose, so setting accepts a 5% chance of this error when the null hypothesis is actually true. For example, convicting an innocent defendant is a Type I error when the null hypothesis is innocence. Lowering reduces Type I errors but makes Type II errors more likely, so the two trade off.
P-value
Hypothesis testing
A p-value is the probability, computed assuming the null hypothesis is true, of getting a result at least as extreme as the one you observed.
A p-value measures how well your data agree with the null hypothesis: a small p-value means the observed result would be surprising if the null were true, which counts as evidence against it. It is a probability about the data, not the probability that the null hypothesis is true. For example, a p-value of 0.02 says that if the null hypothesis held, data at least this extreme would occur about 2% of the time. You reject the null hypothesis when the p-value is at or below the significance level (the Greek letter alpha).