Features of a Good Study on Illness
- There are a number of factors to consider when evaluating the design of a study
- The sample of people included in the study
- Sample size should be representative of the population involved; larger samples are more likely to be representative
- Randomly selecting participants removes bias and increases the likelihood of a representative sample
- Selecting people from among friends or from among people at a gym, for example, is likely to select people who have similar lifestyles; this would be a biased sample
- Control variables; the more variables that have been controlled, the more reliable and valid the data
- Reliable data can be reproduced by repeating an experiment
- Valid data has only tested one independent variable whilst all other variables are controlled
- Avoiding bias; there should be no bias involved in the collection or analysis of data
- Bias in data can come from human sources, e.g. by selecting a non-random sample or manipulating data to emphasise a certain outcome
- Bias can be a problem when, e.g. scientists are employed by a company that desires a particular result, or when scientific funding is dependent on specific outcomes
- Controls; the use of an experimental control provides a point of comparison and ensures that the results are due to the variable of interest
- An experimental control condition in a study should be the same as every other condition except that the independent variable should be absent, e.g. in a drug trial the control group would be identical to the group receiving the trial drug, except that they would be given a sugar pill instead of the drug; this is known as a placebo
- Repetition; repeats of the data need to be taken within a study, and similar results should be collected
- Similar results are reliable
- Reproducible; it should be possible to reproduce a set of findings by repeating an entire investigation
- This is why scientists always write up the method used in a study
- The sample of people included in the study
