4. A good (well designed) Experiment
1. Have a clear specification of the aims of the experiment.
2. Be unbiased
Bias may result in false positive results when the effects of some other factor are confounded (mixed with) the treatment effect. It is avoided by correct identification of the experimental unit, blinding, and by randomisation
Bias is minimised by 1. correct choice of the experimental unit, 2. randomisation of the units to treatments and in the order in which subjects are housed and outcomes are measured, and 3. blinding where possible, using coded samples.
3. Be powerful
Power is increased by 1. Larger sample sizes, 2. Good control of variability and 3. Use of sensitive subjects. However, large sample sizes cost animals and money so emphasis should be placed on the last two of these.
4. Have a wide range of applicability
5. Experiments should be simple
Clearly written protocols and stand operating procedures should be used. In some cases it may be necessary to work to “Good Laboratory Practice” standards. http://en.wikipedia.org/wiki/Good_Laboratory_Practice
6. It should be possible to statistically analyse the result of an experiment.
An investigator should never start an experiment without knowing how it is going to be analysed. The results of each experiment should be analysed before starting the next one so that the findings from the first experiment can be taken into account. The most powerful available statistical methods should be used, such as parametric rather than non parametric tests, where applicable..
Internal and external validity
An experiment can be said to have high internal validity if it has a high probability of getting the correct answer. Basically, this means that it should be unbiassed and powerful so that it is unlikely to produce either a false positive or a false negative result.
An experiment will have high external validity if the results can be generalised to other conditions or situations. Note that it can not have high external validity unless it first has high internal validity. The use of randomised block designs (which can sample a range of environments) and factorial experimental designs can be used to increase external validity. Repeating an experiment in another laboratory by other investigators will also increase external validity, assuming the results are repeatable.
As an example, an experiment which uses only a single strain of mice may have high internal validity, but if the same results are not seen with other strains of mice, then it will have low external validity.
It is acceptable to do an experiment with high internal validity but no exploration of its external validity, provided it is made clear that the external validity is unknown. But note that in many cases randomised block and factorial designs can be done at little or no extra cost.