A statistical test is always a test on your Null Hypothesis and tests the probability that your Null Hypothesis is valid. The 5 most commonly used statistical tests are:
- Standard t-test
- Paired t-test
- One-way Analysis of Variance (ANOVA)
- Two-way ANOVA
- Linear Regression
Each of these five tests is a statistical comparison of two (or more) means from each separate group in the experiment.
The standard t-test is used to compare the means from exactly two groups, such as the control versus the experimental group.
There are two types of categorical variables: a nominal categorical variable and an ordinal categorical variable. A nominal variable is a categorical variable without an implied order. This means it is impossible to say that 'one is worth more than the other'. Think for example of the categorical variable animals_vector, with the categories "Elephant", "Giraffe", "Donkey" and "Horse". Here, it is impossible to say one stands above or below the other. In contrast, ordinal variables do have a natural ordering. Consider for example the categorical variable temperature_vector with the categories: "Low", "Medium" and "High". Here it is obvious that "Medium" stands above "Low", and "High" stands above "Medium".
- Multivariate analysis (MVA) refers to methods for the analysis of data containing more than one variable
- One of the problems with MVA is data visualisation since it becomes difficult to visualise three or more variables
- Research methods: Know when your numbers are significant - http://www.nature.com/nature/journal/v492/n7428/full/492180a.html
- Explained variation - https://en.wikipedia.org/wiki/Explained_variation
- Percentage of variance explained is the ratio of the between-group variance to the total variance, also known as an F-test.