3.2 - Sampling Bias3.2 - Sampling Bias
Recall the entire group of individuals of interest is called the population. It may be unrealistic or even impossible to gather data from the entire population. The subset of the population from which data are actually gathered is the sample. A sample should be selected from a population randomly, otherwise it may be prone to bias. Our goal is to obtain a sample that is representative of the population. In Jaylen’s case, he needs to obtain a sample of nurses that are representative to the population of nurses.
- Representative Sample
- A subset of the population from which data are collected that accurately reflects the population
If Jaylen fails to appropriately sample, he risks biasing his results due to sampling bias. Sampling bias occurs when there is a systematic favoring of certain outcomes due to the methods employed to obtain the sample.
Jaylen needs to be aware of three main sources of bias.
Types of Bias
- Non-response – large percentage of those sampled do not to respond or participate.
- Response – when study participants either do not respond truthfully or give answers they feel the researcher wants to hear. For example, when students are asked if they ever cheated on an exam even those who have would respond with "no".
- Selection – this bias occurs when the sample selected does not reflect the population of interest. For instance, you are interested in the attitude of female students regarding campus safety but when sampling you also include males. In this case your population of interest was female students however your sample included subject not in that population (i.e. males).