# Experiments

## How to analyze it?

**Tests of statistical inference are used to analyze experimental results.** The purpose of using these tests is to determine how likely it is that the result obtained in the study is due to chance or reflects a real difference. When using inferential statistics to analyze the results, the following steps should be taken:

- Determine the type of experimental design used.
- Determine the number and levels of the independent and dependent variables.
- Determine the type of statistical test. Inferential statistic tests can be divided into two types: Parametric and Non-parametric. Parametric tests make certain assumptions about the parameters of the population from which the sample has been drawn. In contrast, Non-parametric tests do not specify any assumptions about the nature of the population. Parametric tests are more powerful (i.e., more likely that the test will detect a difference) and robust, thus it is always preferable to use them instead of non-parametric tests. However, given their assumptions, parametric tests can only be used when the following parameters are met:

__The dependent variables have equal units of measurement__ (e.g., temperature, weight, time);

__The data is drawn from a normally distributed population.__ This can be checked through statistical analysis of the data, using the Kolmogorov-Smirnov and the Shapiro-Wilk Tests;

__There is similar variability between sets of scores that have been collected.__ This can be checked through statistical analysis of the data, using the Levene’s Test.

__If these three conditions are met, parametric tests should be used__ as they provide a more accurate estimate of the probability that the result is due to chance instead of reflecting a real difference.

**Choose the specific statistical test to analyze data.**This decision should be based on the experimental design used in the study, the number and type of independent and dependent variables, and the type of statistical test.

**Determine if the level of significance reached is “acceptable” to reject the hypothesis that the obtained difference is due to chance.**By convention, if the level of significance reached is below 5 percent or less, the hypothesis that the differences obtained are due to chance is rejected.