Parametric data refers to data that follows a normal distribution and can be analyzed using traditional statistical methods such as t-tests and ANOVAs. Non-parametric data, on the other hand, does not follow a normal distribution and requires alternative statistical methods such as Mann-Whitney U tests and Kruskal-Wallis tests.
The best way to determine if your data is parametric or non-parametric is to plot it on a histogram and visually inspect its distribution. Additionally, you can use statistical tests such as the Shapiro-Wilk test or the Kolmogorov-Smirnov test to formally test for normality.
The main advantage of using parametric tests is their greater statistical power, meaning they are more likely to detect a significant difference if one exists. However, they also have stricter assumptions that must be met in order to produce accurate results. Non-parametric tests, on the other hand, have fewer assumptions and can be used with non-normal data, but they have less statistical power.
You should use parametric tests when your data is normally distributed and meets the assumptions of the test you are using. If your data does not meet these assumptions, you should use non-parametric tests. It is also important to consider the type of data you have and the research question you are trying to answer when deciding which type of test to use.
In some cases, it may be possible to transform non-parametric data to make it more normally distributed. However, this should only be done if it is appropriate for the type of data you have and the research question you are trying to answer. It is important to consult with a statistician or conduct thorough research before attempting to transform your data.