What are parametric and nonparametric inferential tests? Solution Inferential statistics suggest statements about a population based on a sample from that population. Parametric inferential tests are carried out on data that follow certain parameters: the data will be normal (i.e. the distribution parallels the bell curve); numbers can be added, subtracted, multiplied and divided; variances are equal when comparing two or more groups; and the sample should be large and randomly selected. There are generally more statistical technique options for the analysis of parametric than non- parametric data, and parametric statistics are considered to be the more powerful. Common examples of parametric tests are: correlated t-tests and the Pearson r correlation coefficient. Inferential statistics suggest statements or make predictions about a population based on a sample from that population. Non-parametric tests relate to data that are flexible and do not follow a normal distribution. They are also known as.

1. What are parametric and nonparametric inferential tests?
Solution
Inferential statistics suggest statements about a population based on a sample from that
population. Parametric inferential tests are carried out on data that follow certain parameters: the
data will be normal (i.e. the distribution parallels the bell curve); numbers can be added,
subtracted, multiplied and divided; variances are equal when comparing two or more groups; and
the sample should be large and randomly selected.
There are generally more statistical technique options for the analysis of parametric than non-
parametric data, and parametric statistics are considered to be the more powerful. Common
examples of parametric tests are: correlated t-tests and the Pearson r correlation coefficient.
Inferential statistics suggest statements or make predictions about a population based on a
sample from that population. Non-parametric tests relate to data that are flexible and do not
follow a normal distribution.
They are also known as