Distribution of Variables

 

It is common practice to take several, replicate, measurements of the same variable - this allows for experimental variations to be taken into account in the analysis.
Before you can decide how to plan your experiment and analyse your data, you need to think about how and why your own data is likely to vary.

 

ACTION: You need to consider

Main Categories of Variability:

Normal Distribution:

 

The starting point is often to ask yourself - 'Is my data normally distributed?'

For example, if you use a spectrophotometer to make repeated (replicate) measurements of the absorbance of the same solution, you might expect to get a range of slightly different values centred symmetrically on a mean value. The greatest number of values would be close to the mean value with a decreasing probability of finding a value at greater distances from the mean.

This is the well-known 'bell-shaped' curve described by the normal distribution.

 

You can link here for further information on performing a Statistical Test for Normality

 

Many techniques of statistical analysis assume that the data is normally distributed, e.g. t-tests, ANOVAs.

 

Thankfully, most repeated experimental variations do follow a normal distribution, particularly when the random variations are small (~<20%) compared to the mean value.

 

Deciding that your data is Not Normal:

 

It is important for you to know if your data is not normally distributed:

Transforming Data to a Near Normal Distribution:

 

For certain non-normal distributions, It is possible to apply a mathematical transformation that will allow the data to be treated as though it were normal.

See 8 Data Analysis > Data Transformation