# Inferential statistics

## lab

### Why?

To test hypothesis basded on a quantitative dataset.

### How?

First, perform descriptive statistics. Next, formulate a hypothesis that can be expressed in variables of your dataset. Test your hypothesis using an appropriate statistical test. Every test makes certain assumptions about the character of yoru data; make sure your data complies with these assumptions.

### Ingredients

- A well-defined dataset and hypotheses
- Statistical software such as SPSS, R, or Excel
- Advanced knowledge about statistics and probability theory
- A keen eye for the difference between signal and noise

### In practice

With the advent of big data, inferential statistics are increasingly important. However, inferential statistics are delicate and companies often use specialists to analyse them. Novices commonly make errors like 'capatilising on chance'. This happens when an analyst tests so many hypothesis that some turn out 'positive' by accident.

### Phase(s) of use

In the following project phase(s) inferential statistics can be used:

- Analysis
- Realisation