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Home , Biostatistics

Biostatistics: p value and Testing

by Dr. Sujata Department of Multanimal Modi College, Modinagar p value and Hypothesis Testing What is the level of significance and p value? The level of significance(alpha) is the percentage of risk we are willing to take while rejecting the null hypothesis. p value is the probability that a random chance generated the or something else that is equal or rarer (under the null hypothesis). We calculate the p-value for the . “p value is the measure of strength of evidence against the null hypothesis” .A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random). Therefore, we reject the null hypothesis, and accept the . .A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. This we retain the null hypothesis and reject the alternative hypothesis. You should note that you cannot accept the null hypothesis, we can only reject the null or fail to reject it. Either of the given three statements can be taken in making alternative Hypothesis: 1)When sample is considered greater than the mean; 2) When sample mean is considered smaller than the population mean; 3) When sample meant value not equal to population mean value. Types of Statistical Test

There are various types of statistical test used to test the hypothesis or to make decision for the hypothesis on the basis of observed data. Z-test: This test is used to compare a sample to a defined population. is given in the problem studied. It is meant to test whether sample belongs to same population or different. Ho: sample mean is same as the population means (Null hypothesis, μ = μo) Ha: sample mean is not same as the population mean (Alternate hypothesis) Formula for the test: z = (x — μ) / (σ / √n) where , x=sample mean, u=population mean, σ / √n = population .

t-test: t test is used to determine if there is a statistically significant difference between two sample group. Sample size is limited less than 30. If the calculated t value exceeds the value at given level, the result is said to be significant and if it is less then reverse is true, result is insignificant i.e. null hypothesis is not rejected. Chi square test: It is a simple statistical analysis used to test null hypothesis. It is a type of test. This test is used to decide whether the difference between expected number and observed number either due to chance or statistically significant.

o= observed number, e= expected number, d is used for o-e stand deviation value In the examination of genetic crosses, chi square test is used to test whether the progeny seems to fit particular ratio (1:1, 3:1, 9:3:3:1) to support null hypothesis or not. If the chi square calculated value is lesser than the critical table value null hypothesis should not be rejected when it come greater reverse is true. When result is statistically significant a new hypothesis or alternative hypothesis must be developed to explain the data.

Anova (Analysis of ): It is used to compare two unrelated groups either these are different or same by using F -distribution. It is of two types, one way and two way Anova. One way anova: two unrelated groups are tested with one independent variable. Two way anova: two unrelated groups are tested with two independent variables.