# Descriptive statistics

## field

### Why?

To get an overview and summary of a dataset.

### How?

Get a grasp of the integrity of your data. Eliminate bogus data-points. Summarise your data with appropriate tables such as counts, frequency charts, means and standard deviation, and graphs such as box-plots and bar and pie charts. Try to get a global sense of what your data is telling you.

### Ingredients

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

### In practice

All statistical analyses should start with descriptive statistics. They give researchers afeel for the data and data integrity. Sometimes, descriptive statistics are sufficient to answer the reasearch questions. In other cases, they are a necessary prerequisite for inferential statistics.

### Phase(s) of use

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

- Analysis

### Scales

inspiration
data

expertise
fit

overview
certainty