**Examples of Experimental Variables
- in development**

**You need to place each of the variables and factors in
your project in one of the data types below and read the accompanying notes
about possible analytical methods**.

**Scale / Continuous data**

Wide variety of experimental results:

Chemistry:- Retention times in GC and HPLC, Wavenumbers in FTIR

Bioscience:- Body fat, Lung function (volumes), Reaction time

Physical science:- Pressure, voltage, current

**Scale / Discrete data**

**Proportions / Percentages **

**Examples:**

Percentage recovery of compound

**Frequency**

**Notes**:

A **chi-squared** analysis
is a common technique, where frequencies are compared for combinations of
different conditions. For chi-squared analysis the data does not need to be
normal.

With sufficient counts and replications it is possible to treat the frequency as a continuous variable, possibly with a normal distribution..

**Examples:**

Counting numbers of hairs

Contractions of pig bladder

**Ordinal data **

**Notes**:

This data is **non-parametric**
in that any value associated with it only gives a **ranking** to the data and
does not have any further quantifiable significance.

Hence it cannot be assumed to
be normally distributed, and it is necessary to use **non-parametric statistics**,
e.g. Mann-Whitney, Wilcoxon, Kruskal-Wallis.

Under certain circumstances you may choose to demonstrate the use of parametric statistics, e.g. ANOVA, for this data, provided you make it clear in your discussion that the results may be unreliable.

**Examples:**

Quality assessment

**Nominal data **

**Notes**:

This data has no quantitative or ranking value. Usually appears as a factor variable in the experiment design.

**Examples:**

Gender (M/F)

Smoker (Yes/No)

Fabric type