Hypothesis means assumption.
To test whether our assumption is correct based on given data is Hypothesis testing.
Take a example of a tire factory. The radius of the ideal tire must be 16 inches. However, even if there is a deviation of 8% then it is accepted. Hence in this scenario, we can apply hypothesis testing.
- Define the Null Hypothesis (H0): The radius of the tire= 16 Inch
- Define the alternate Hypothesis(Ha): The radius of the tire != 16 Inch
- Define the error tolerance limit: 8%
- Conduct the test
- Look at the P-value generated by the test: P-value= 0.79
- If P-Value > 0.05 then accept the Null Hypothesis otherwise reject it. : Accept the Hypothesis, Hence, The tire produced is of good quality
P-Value is the probability of H0 being True.
The higher the P-value, the better the chances of our assumption(H0) to be true. The Textbook threshold to reject a Null Hypothesis is 5%. So, if P-Value is less than 0.05, this means there is less than 5% chance of Null Hypothesis being true, hence it is rejected. Otherwise, if P-Value is more than 0.05, then the Null Hypothesis is accepted.