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Tests of Significance

Once a hypothesis is made, the next step is to test it by means of various tests (known as tests of significance). These can be broadly classified.
  1. Parametric tests:
    1. These are –
      1. t test,
      2. z-test
      3. ANOV A (or F-test)
    2. These share few common features:­
      1. These hypothesis refers to certain population parameters which is the population mean (in case of t and z tests) or the population variance (in case of F test).
      2. These hypothesis concern interval or ratio scale data (e.g. weight, height, BP etc).
      3. They assume that the population data are normally distributed.
  2. ​​Nonparametric tests:
    e.g. - Chi square test.
    1. They do not test hypotheses concerning parameters.
    2. They do not assume that the population data is normally distributed hence - distribution free tests.
    3. They are used to test nominal or ordinal scale data.

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