Type I Error (False Positive): This error occurs when we think something is true (like believing there is a monster under the bed), but it's actually false. Specifically, it means that we reject the null hypothesis (H0) when it is true. An example would be concluding a new drug is effective when it is not.
Type II Error (False Negative): This error happens when we think something is false (like not believing there is a monster under the bed), but it is actually true. It means we fail to reject the null hypothesis (H0) when it is false. For instance, it would be concluding that a new treatment is ineffective when it is actually effective.
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