- Hypothesis testing deals with confidence level of the sample collected vs the population in consideration. Basically, the mean calculated for a population does not necessarily match with the mean calculated for the sample. So based on this, we need to see the confidence level of the sample in regards to population.
- Hypothesis is a conjecture, an assumption or a statement which may or may not be TRUE
- Statistical Hypothesis is a statement about a Population which may or may not be TRUE
NULL and ALTERNATE HYPOTHESIS
- A NULL Hypothesis is status quo.
- Rejection of NULL Hypothesis leads to desired conclusion i.e. ALTERNATE Hypothesis
- What you intuitively feel is TRUE is the ALTERNATE Hypothesis. This forms "What you wish to prove."
- For Example
- NULL hypothesis H(0): Ram is liar
- ALTERNATE hypothesis H(A): Ram speaks the truth
- Rejection of NULL Hypothesis when it is TRUE is TYPE I error - Alpha
- (1 - Alpha) is the probability of accepting the NULL hypothesis when its TRUE
- Alpha is the Level of Significance of the test
- (1 - Alpha) is the Confidence Level of the test
- Acceptance of NULL Hypothesis when it is FALSE is TYPE II error- Beta
- (1 - Beta) is the Power of the test - How clear is the demarcation between NULL and ALTERNATE hypothesis
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