Errors in Statistics
(The Type I and Type II Error in Statistics)
“An error does not become truth by reason of multiplied propagation,
nor does truth become error because nobody sees it…”
For the better understanding of statistical errors, it is essential to understand the concept of ‘Level of significance’, ‘Null hypothesis and ‘Alternate hypothesis’.
What is ‘Level of Significance?
Ø Definition: The Level of significance is the probability of rejecting the null hypothesis in a statistical test when it is true.
Ø The ‘Level of Significance’ in statistics is conventionally set to 0.05 to 0.01.
Ø The level of significance in statistics denotes the confidence level of an investigator to accept or reject a null hypothesis in the statistical testing.
Ø A level of significance 0.05 denotes 95% confidence in the decision whereas; the level of significance 0.01 denotes 99% confidence.
Ø Such a low level of significance is selected to reduce the erroneous rejection of a null hypothesis (H0) after the statistical testing.
What is Null hypothesis?
Ø Definition: The Null hypothesis is a statement that one seeks to nullify with evidence to the contrary.
Ø The ‘Null hypothesis’ is denoted as H0.