Which Test to Use One of the realities of biostatistics is that the best specifi

Question

Which Test to Use One of the realities of biostatistics is that the best specific test or analysis to use is not always obvious: ANOVA versus t-test? Parametric versus non-parametric test? e Chi-square versus probability analysis? Consider your workplace, or other public health situations you are familiar with, and identify a situation where it might it be appropriate to use a probability assessment over a chi-square analysis. Describe the situation and explain why you think the probability assessment is the better approach

Explanation / Answer

(i) ANOVA verses t test-

                 t test is used when the population means of only two groups is to be compared. but ANOVA is used when means of more than two groups are to be compared. t-distribution is used when the standard deviation is unknown, and the sample size is small. t test has following assumption-sample is drawn should be normally distributed, homogeneity of variance, random sampling of data, independence of observations.

example- null hypothesis is of H0: µ(x) = µ(y) against alternative hypothesis H1: µ(x) µ(y), wherein µ(x) and µ(y) represents the population means. The degree of freedom of t-test is n1 + n2 – 2

                 ANOVA is used when comparison is to be made between more than two population means.in ANOVA is assumed that the sample is drawn from the normally distributed population and the population variance is equal.

example-more than two population means like the yield of the crop from multiple seed varieties, say S1,S2,S3,S4.

(2) non parametric verse parametric-

use nonparametric tests when your data don’t follow the assumptions of the parametric test, especially the assumption about normally distributed data. in other word we do not know the parameters and population of data. in nonparametric null hypothesis is free from parameter.Parametric analysis to test group means

use parametric tests when your data follow the assumptions of the parametric test, especially the assumption about normally distributed data. in other word we completly population distribution and parametric.null hypothesis involves the parameter.Nonparametric analysis to test group medians.

(3) Chi-Square test is a non-parametric test that is used to find out how the observed value is significantly different from the expected value. In Chi-Square test, the term goodness of fit is used to compare the observed sample distribution with the expected probability distribution. Chi-Square goodness of fit test determines how well theoretical distribution (such as normal, binomial, or Poisson) fits the empirical distribution.

          chisquare=sum(o-e)^2/sum(e)

probability is used to solve problems where the probability of sets of unordered outcomes is considered. example, to calculate the probability that two carriers of a recessive disease will have four children, two affected and two healthy, in any order.

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