in statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others.
The word bias could mean:
- in sampling: give preference to selecting some individuals over others
- in statistic: certain responses are more likely to occur in the sample than in the population (statistic is the numerical summary of a sample)
Sources of bias:
- Sampling bias
- Nonresponse bias
- Response bias
Sampling bias means that the technique used to obtain the sample’s individuals tends to favor one part of the population over another. Sampling bias also results due to under-coverage, which occurs when the proportion of one segment of the population is lower in a sample than it is in the population.
Nonresponse bias exists when individuals selected to be in the sample who do not respond to the survey have different opinions from those who do.
Response bias exists when the answers on a survey do not reflect the true feelings of the respondent.
Nonsampling errors result from data-entry error, undercoverage, nonresponse bias, response bias. Such errors could also be present in a complete census of the population. Sampling error results from using a sample to estimate information about a population. This type of error occurs because a sample gives incomplete information about a population.