How to Identify Type 1 and Type 2 Errors in Statistics

To understand the rest of this video think of the Null Hypothesis as the “STATUS QUO” and rejecting the null hypothesis as accepting a new observation instead of the status quo.
A Type 1 Error is also known as a False Positive and occurs when we reject the null hypothesis, even when it is true. If we instead Accept or “fail to reject”, the Null Hypothesis even though it is false, this is a Type 2 Error.
Let’s use an example to further drive this home:
A woman is being charged with murder but she claims to be innocent. In this case, the Null Hypothesis can be summarised wonderfully with the phrase “Innocent until proven guilty”. This means that the Alternative Hypothesis is that she is indeed guilty.
Now a type 1 error or false positive in this case be if the woman is thrown in jail even though she is innocent. A type 2 error in this situation would be if she is found innocent, even though she is guilty. As you can see, both of these errors are less than ideal!

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