Experiments

Statistics is often used to analyse data from experiments, so its important to know how to gather the data in ways that avoid bias and aren't affected by lurking variables.

Factors - The explanatory variables in experiments.

Treatments - Anything we do to change an explanatory variable to see how it affects a response variable.

Design - The way treatments are assigned to individuals in an experiment.

Double Blind Experiments
A double blind experiment is on in which neither the subjects, nor the people administering the treatment, know what treatment is given to what subject. This is done so that a subjects response wont be influenced by what type of treatment they think they're receiving. It is a way of eliminating bias from an experiment.

Comparative Experiments
Experiments in which different individuals or groups of individuals are given different (or no) treatments, and their responses compared. This is done to control the effects of lurking variables. By comparing two identical individuals/groups in exactly the same setting, with the only difference being the treatment they receive, we know that any difference in their responses can only be due to the difference in their treatments.

Contol - An individual present in a comparative experiment solely for the purpose of comparison. Controls as their name implies control the effects of lurking variables.
Control Group - A group of individuals that acts as a control.

Randomised Comparative Experiments - Comparative experiments in which the treatments are assigned to individuals randomly.
In this type of experiment, the more subjects in each treatment group (a group of individuals receiving the same treatment) the less chance outcomes will affect the experiments results. Differences in individual results will affect the average result of the group less if the group is larger.

Statistical Significance
When there is a large difference in the responses of two large treatment groups in a randomised comparative experiment, it is unlikely to be due to chance. An effect of a treatment is statistically significant if it is large enough that it would be unlikely to occur by chance.

Completely Randomised Design - A design for randomised comparative experiments where the treatment groups are all of equal size.

Block - A group of individuals in an experiment known beforehand to be similar in a way expected to affect their response to a treatment, e.g. an experiment on humans might put the men into one block and the women into another block.

Block Design - An experiment design where treatments are randomly assigned to individuals within blocks. The way the individuals in each block are affected by the treatments is then compared.

Matched Pairs Design - A type of block design in which pairs of similar subjects are used to compare two treatments. Each subject in a pair receives a different treatment from the other. The "pair" in a matched pair experiment can be a single subject who is given both the treatments. In such a case, the order in which the treatments are administered should be random in case the first treatment affects the results of the second.

Lack of Realism
Experiments can suffer from a lack of realism in that the idealised settings created for the experiments may give results that are not applicable in real-world situations.