Before conducting an experiment, several decisions must be made. These are essentially non-statistical decisions that must be based on knowledge of the subject area being studied, though some understanding of statistics can help to guide them.

Purpose

A clear statement of the objectives of the experiment is important. In defining the goals of the experiment, it is important that people with intimate knowledge of the process or subject area are included in the team that is charged with designing and running the experiment.

Many experiments are intended to solve a problem. After recognising that there is a problem, it needs to be carefully defined. Stating the problem clearly and obtaining general agreement that this statement really does describe the problem is imperative. Quite frequently,...

A clear statement of the problem can lead to process improvement without any experimentation, simply through creating a greater understanding of the process.

What experimental units should be used?

In some types of experiment, there is an obvious choice of experimental units. For example, an experiment to assess the effect of a functional ingredient of a cat food on its consumption would use individual cats as experimental units.

However in horticulture, the experimental units are often plots of land whose area is relatively arbitrary. In a similar way, experiments on food products must decide on the volume or weight of product to be used as experimental units.

It is desirable for experimental units to be as similar as possible, so every attempt should be made to make the experimental units homogeneous. We should therefore characterise the process in terms of 'nuisance' variables and endeavour to find ways of minimising their variability for the experiment.

Variability is the enemy of the experimenter!

Large experimental units often 'average out' the variability between experimental units, but larger experimental units are also more costly.

What response variable should be recorded?

In an experiment, there is sometimes a single obvious response measurement from an experimental unit (e.g. crop yield per square metre, concentration of impurities, bacteria count), but often there are several variables which can be considered as response measurements.

For example, in a study of how a fertiliser affects growth of tomato plants, how do you measure growth?

There are many possibilities here and a biologist would need to decide on which was most important from a biological perspective.

Which variables should be controlled?

Thought also needs to be given to which variables will be controlled in the individual experimental units. In an agricultural experiment, do we only want to assess the difference in yields for three crop varieties, or do we simultaneously want to determine the effects of different levels of application of fertiliser?

For numerical controlled variables (e.g. temperature, storage time, concentration of an additive), a decision must also be made about which values should be used in the experiment. These values should cover the region of practical importance.

The controlled variables and are called factors in the context of an experiment. The combinations of factor values (also called factor levels) that are used for different experimental units are called experimental treatments.

Each experimental unit receives some treatment.