Pilot studies are mini-versions of what is to be the main experiment. It can help test out the environment or variables and try to fine tune the final experimental design. Pilot studies are useful as part of very large experiments that cannot afford to make silly errors. They can also provide some insight when a full-scale experiment would be too complex or costly.
Pilot studies can provide proof-of-principle for a certain experimental approach at a much lower expense than the fully fledged project would. They can also crystallise the appropriate range of values for certain variables. This minimises the different trials required in the full experiment.
Variables are factors present in an experiment. Often, they are the factors that we are primarily interested in e.g. testing a food variable against an age variable. Due to the nature of experiments taking place in the actual world, many confounding variables can present themselves, more or less clearly, in experiments. Confounding variables are those variables which can overlap so that it cannot be obvious whether a result is due to one variable or another. For example, in looking at people’s diets which contain both fat and sugar, these components are confounding variables of other in a study that wants to only look at the effect or fat or sugar.
In order to tease away confounding variables from each other, a technique called randomised block design is used to categorise data into groups, and then carry out the experiment independently in each. For example, participants can be split by weight, diet, sex, height, etc. so that any findings can be said to not be due to any of these pre-split factors.
Variables vary by the type of data they can produce. Discrete variables give rise to individual data points that cannot be connected….