<Details

# Descriptive statistics

## field

### Why?

To get an overview and summary of a dataset

### How?

Get a grasp of the integrity of your dta. Eliminate bogus data-points. Summarize 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 of 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 a feel for the data and data integrity. Sometimes, descriptive statistics are sufficient to answer the research 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:

- Problem definition
- Analysis