# 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. â€‹These are â€“
1. â€‹t test,
2. z-test
3. ANOV A (or F-test)
2. â€‹These share few common features:Â­
1. â€‹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. â€‹â€‹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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