By Ahmad Jamil Malik

P value for hypothesis testing

• We find statistical association in the form of p value to see if the risk factor has a significant relation with the disease.
• The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H₀) of a study question is true.
• Smaller the p value, greater is its significance

Null Hypothesis (H₀):

In null hypothesis, we assume that there is no relationship between the two factors under observation (outcome and effect), ‘no effect of treatment’, ‘no difference in survival rates’, ‘no difference in prevalence rates’

Alternative Hypothesis (H_a):

In alternative hypothesis, we assume that there is a relationship between the two factors under observation (outcome and effect).

Significance Factor:

The choice of significance level at which you reject H₀ is arbitrary. Conventionally the 5% (less than 5 in 100 chance of being wrong, 1% and 0.1% (P < 0.05, 0.01 and 0.001) levels have been used.

Type I Error:

• Rejecting the null hypothesis when it is true is called a type I error.
• The probability of committing a type I error is represented by α and is called the significance level of the test.
• Example: If α = 0.05 and if repeated tests of hypothesis are conducted based on samples of size n, a true null hypothesis would be rejected 5% of the time.

Type II Error:

• Failing to reject the null hypothesis when it is false is called a type II error.
• The probability of making a type II error is denoted by β.