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About Individual Tests

  1. T -test: (student t-test)
    As for all parametric tests, it is used for quantitative data which is normally distributed in the population.
    (i.e. continuous data such as weight/height etc).
    T -test can be unpaired or paired:
    Paired t-test is a special type of t-test used when the data is "paired" e.g. - blood glucose before and after taking a drug OR Pulse rate before and after exercise (in the same group).
  2. Z-Test:
    1. Involves the same steps as t-test.
    2. Used when the sample is large (n> 100).
    3. T-test though is more important as there is no ­situation where as Z test can be used and a t-test cannot be but the vice versa can occur.
  3. Anova:
    Whereas a T test is appropriate for making just one comparison (between two sample means, or between a sample mean and a hypothesized population mean), when more than one comparison is being made (i.e. when means of> 2 groups are being compared), ANOVA (analysis of variance) is the appropriate technique.
    ANOV A can be:
    1. One way ANOVA - when the groups differ in terms of only one factor at a time.
      e.g.: 3 different drugs given to 3 groups of people to compare their effects.
    2. Two way ANOVA - If the groups 2 differ in factors at a time.
      e.g. - If in the above example if each group consists of male and female patients, the researcher may also want to make comparisons between the sexes, making a total of 6 groups.
  4. Chi-square test:
    Used for qualitative data, which is in the form of proportions (percentages). e.g. – comparing incidence of a disease in 2 groups of children with one group vaccinated and the other not, OR com­paring incidence of Hb < 10 (anemia) in 2 groups of pregnant women, one taking iron supplements and the other not.

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