Many students (and researchers) are often daunted by the multitude of available statistical techniques they could use to analyse their data. How do they know they’ve picked the right technique? And, how do they use and interpret the technique?
It may surprise you to know that the choice of statistical technique actually starts with the research design, hence, one should make a choice about the technique(s) you will use as part of the research design process. Formulating a research question begins the process of statistical analysis (it establishes the statistical hypotheses you will test) and deciding how you will collect data in an attempt to answer the research question (i.e. the research design) specifies the levels of measurement for each study variable (i.e. nominal, ordinal, interval or ratio) and therefore what statistical techniques are appropriate.
The following attempts to present the common statistical techniques that students meet in their undergraduate studies, ordered by the way they are typically introduced to undergraduate students.
Techniques by name
- Chi-Square Goodness-of-fit test
- Chi-Square Test of Independence
- One-Sample Z test
- One-Proportion Z test
- Two-Proportion Z test
- One-Sample t test
- Paired t test / Repeated-Measures t test
- Two-Sample t test / Independent Samples t test
- One-way ANOVA
- Two-way ANOVA
- Simple Linear Regression
- Multiple Linear Regression
Last updated: 10 September 2020