Sampling Bias – Definition, Types and Examples You Must Know

This article will describe the definition of sampling bias and its types, along with some generic examples. Let’s move on without taking too much time.

Sampling Bias – A Brief Introduction

Sampling bias occurs when the likelihood of participation of some members in a population is greater than others. It occurs when researchers and observers intentionally prefer some members of a population over others. It results in biased samples, including data from participants that do not best represent the whole population. Thereby, the results extracted by analysing biased data do not predict useful relationships and trends among variables involved in a study.

To avoid it, a researcher must have to select all the participants randomly, even though it is really hard to give equal opportunities to all the participants of a population to participate in a study. Even in non-random selection of samples, you can employ bias reduction ways in the data set. The following is a brief description of types of sampling bias and examples so you can know more about it,

Types Of Sampling Bias:

Sampling is a critical phase of a research process that allows the researcher to use different types of sampling techniques such as probability or non-probability. In each different type of sampling, the associated sampling bias can be reduced in number of ways. In academic research, to deal with bias associated with each type of sampling, you can find dissertation writers UK help.   However, reducing the sampling bias is the main concern in both random as well as non-random sampling. The sampling bias is mainly of four types, undercoverage, self-selection, observer bias, and recall bias. The following is a brief description of each, along with examples.


This type of bias happens when a researcher chooses an inadequate number of participants to be involved in a study. It occurs at the time of selection of sample size. The small sample size increases the chances of skipping many potential candidates having unique properties to be included in a study. For example, if you are trying to study how evolution takes place, then the characteristics of individuals of each era by selecting an appropriate sample size is necessary. Inadequate sampling or undercoverage may prevent you from considering all characteristics causing biased results.


It occurs when the researcher independently makes the decision to select participants for a study. Self-selection may result in the collection of data from participants that appeal to the researcher in any way. However, such appealing factors may also have a negative effect on the results of the study. For example, in a call-in radio survey, sampling bias occurs as there are no criteria to treat the callers equally. The generalisation of survey results conducted by radio or TV programs can easily be challenged due to high chances of self-selection.

Observer Bias:

This type of bias occurs from the observer’s side when they intentionally or unintentionally try to project their expectations on the research. Having vast working field experience in an area of interest and extensive literature knowledge are both main reasons behind the observer sampling bias. For example, in experimental research, students often manipulate some variables to get the results of their own choice; that is the best example of biasness from the observers’ own side.

Recall Bias:

This bias is often seen in interviews and survey situations. This happens if a respondent fails to remember past events correctly and gives a response either to prove him a respected man or to impress the interviewer. Recall bias is most challenging to eliminate or even reduce. This is because you cannot force your respondent to recall their past and give honest responses. For example, if you want to know about survivors of war then the results of your research solely depend on the recalling abilities of respondents, and if they fail to remember some incidents, it may cause recall bias.

For conducting dissertation research, you must know about the different types of sampling bias associated with different research designs. In actuality, it is near impossible to avoid all potential biases, but at least we try to minimise them as much as possible. However, the above-mentioned are a few biases associated with the primary research design, but they must be avoided in the secondary research design as well. To get a complete guide to avoiding bias in secondary research design, you must seek expert dissertation help UK.

Bottom Lines:

In a nutshell, bias is an inclination or prejudice act that must be avoided throughout the research process. However, sampling is the step of research that requires the researcher to become more conscious about the measures to avoid bias. The article has provided enough details to know sampling bias and ways to avoid it. Stay in touch to get more content in similar niches.