Answer:
Statistical error is the difference between the estimated or approximated value and the true value.
<u>Two Possible Types of Statistical Error</u>
Type I Errors occur when we reject a null hypothesis that is actually true; the probability of this occurring is denoted by alpha (a).
Type II Errors are when we accept a null hypothesis that is actually false; its probability is called beta (b).
<u>Example </u>
You test whether a new drug intervention can alleviate symptoms of an autoimmune disease.
A Type I error happens when you get false positive results: you conclude that the drug intervention improved symptoms when it actually didn’t. These improvements could have arisen from other random factors or measurement errors.
A Type II error happens when you get false negative results: you conclude that the drug intervention didn’t improve symptoms when it actually did. Your study may have missed key indicators of improvements or attributed any improvements to other factors instead.
