There are many statistical measure that fall under the umbrella of descriptive statistics such as variance, mean, median and standard deviation. These measures are often divided into one of two categories:
1. Measures of Central Tendency: Measures of Central Tendency represent all of the observations with one number such as a mean and this can be misleading. The most commonly used measures of central tendency are the mean and the median.
2. Measures of Dispersion: Measures of Dispersion show how spread out the data is. Examples of measures of variation or scale include variance, standard deviation, IQR, Winsorized variance. Newer methods include TBS (translated biweight S) estimator and tau measure of scale which are better when trying to protect against outliers.
Inferential statistics allow you to make inferences about the population. Two different inferential statistics that will be covered in this online guide are tests of difference and regression.
1. Tests of Difference: t-tests, ANOVA, Chi-square are among the most common tests of difference. For non-parametric data, Mann-Whitney, Wilcoxon, Kruskal-Wallis and Friedman tests are used.
2. Regression: Simple linear regression and multiple regression are commonly used methods.
Mathematical shorthand is used throughout statistics. X is often used to represent an independent variable and Y is often used to represent a dependent variable. In addition you are likely to see Greek letters like μ (pronounced mu) which signifies the population mean.