A statistical error, in simple words, is the difference between a measured value and the actual value of the data that was gathered.
A hypothesis test can result in two types of errors.
1. Type 1 error
2. Type 2 error
Type 1 Error: A Type-I error occurs when sample results reject the null hypothesis despite being true.
Type 2 Error: A Type-II error occurs when the null hypothesis is not rejected when it is false.
In other words, In statistics, a Type I error is a false positive conclusion, while a Type II error is a false negative conclusion.
The significance level, or alpha (), determines the likelihood of a Type I error, whereas beta () determines the likelihood of a Type II error. These risks can be reduced by carefully designing the layout of your study.
Example: Type I vs Type II error:
You have mild symptoms of COVID-19 and your doctor advised you to go for a test. The following two errors could potentially occur:
Type I error (false positive): the test result says you are COVID positive, but you actually don’t.
Type II error (false negative): the test result says you are COVID negative, but you actually do
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