* Always when the assumptions for the specific test (as there are many parametric tests) are fulfilled.
* When you want to say something about a statistical parameter.
Parametric.
A classic would be the Kolmogorov-Smirnov test.
If the distribution is parametric then yes.
yes
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
Parametric.
A classic would be the Kolmogorov-Smirnov test.
If the distribution is parametric then yes.
yes
Parametric statistical tests assume that your data are normally distributed (follow a classic bell-shaped curve). An example of a parametric statistical test is the Student's t-test.Non-parametric tests make no such assumption. An example of a non-parametric statistical test is the Sign Test.
Parametric tests assume that your data are normally distributed (i.e. follow a classic bell-shaped "Gaussian" curve). Non-parametric tests make no assumption about the shape of the distribution.
Parametric for one set?! Yeah
Binomial is a non- parametric test. Since this binomial test of significance does not involve any parameter and therefore is non parametric in nature, the assumption that is made about the distribution in the parametric test is therefore not assumed in the binomial test of significance. In the binomial test of significance, it is assumed that the sample that has been drawn from some population is done by the process of random sampling. The sample on which the binomial test of significance is conducted by the researcher is therefore a random sample.
It is not.It is not.It is not.It is not.
The Fisher F-test for Analysis of Variance (ANOVA).
t-test
A parametric test is a type of statistical test that makes certain assumptions about the parameters of the population distribution from which the samples are drawn. These tests typically assume that the data follows a normal distribution and that variances are equal across groups. Common examples include t-tests and ANOVA. Parametric tests are generally more powerful than non-parametric tests when the assumptions are met.