Beating bias

Researchers try to keep things objective

Bias is anything that introduces errors into research and distorts your findings. Good design means trying as much as possible to eliminate bias throughout the experiment – from the initial research through to the publication of the results.

Researchers try to reduce bias in several ways. These include using blind trials, in which certain information is kept from people in a study or even the investigators (eg patients not being told whether they are receiving an experimental drug or proven drug). Researchers also use control groups: the control group is treated the same as the experimental group, except in the one variable you are investigating.

If a population is being sampled, the sample size needs to be big enough to reflect the overall population as precisely as possible. This increases the study’s reliability (how likely it is that someone repeating the experiment would get results similar to those of the initial investigator), but it often adds to the cost.

How the sample is chosen is also important. Choosing the sample randomly or systematically helps to eliminate investigator and other biases. As the name suggests, systematic sampling uses a system. You break a population into elements that are then selected at regular intervals to form the sample – for example, from a list of everyone in Year 12, start with a randomly selected student and then pick every 20th student from the list.

Lead image:

Thomas Hawk/Flickr CC BY NC

About this resource

This resource was first published in ‘Number Crunching’ in June 2013.

Statistics and maths, Medicine
Number Crunching, Populations
Education levels:
16–19, Continuing professional development