There is a possibility of bias element in deliberate sampling. Give the reason.
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Sampling bias means that the samples of a stochastic variable that are collected to determine its distribution are selected incorrectly and do not represent the true distribution because of non-random reasons. Let us consider a specific example: we might want to predict the outcome of a presidential election by means of an opinion poll. Asking 1000 voters about their voting intentions can give a pretty accurate prediction of the likely winner, but only if our sample of 1000 voters is 'representative' of the electorate as a whole (i.e. unbiased). If we only poll the opinion of, 1000 white middle class college students, then the views of many important parts of the electorate as a whole (ethnic minorities, elderly people, blue-collar workers) are likely to be underrepresented in the sample, and our ability to predict the outcome of the election from that sample is reduced.
In an unbiased sample, differences between the samples taken from a random variable and its true distribution, or differences between the samples of units from a population and the entire population they represent, should result only from chance. If their differences are not only due to chance, then there is a sampling bias. Sampling bias often arises because certain values of the variable are systematically under-represented or over-represented with respect to the true distribution of the variable (like in our opinion poll example above). Because of its consistent nature, sampling bias leads to a systematic distortion of the estimate of the sampled probability distribution. This distortion cannot be eliminated by increasing the number of data samples and must be corrected for by means of appropriate techniques, some of which are discussed below. In other words, polling an additional 1000 white college students will not improve the predictive power of our opinion poll, but polling 1000 individuals chosen at random from the electoral roll would. Obviously, a biased sample may cause problems in the measure of probability functionals (e.g., the variance or the entropy of the distribution), since any statistics computed from that sample has the potential to be consistently erroneous.
Contents
[hide]
1 Causes of sampling bias
2 Correction and reduction of sampling bias
3 Sampling bias, sampling error, bias of probability function, and limited sampling bias
4 The effect of limited sampling on the determination of statistical and causal relationships
5 Sampling bias in neuroscience
6 References
7 External links
8 See also
Causes of sampling bias
A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Sampling bias is also particularly prominent whenever researchers adopt sampling strategies based on judgment or convenience, in which the criterion used to select samples is somehow related to the variables of interest. For example, referring again to the opinion poll example, an academic researcher collecting opinion data may choose, because of convenience, to collect opinions mostly from college students because they happen to live nearby, and this will further bias the sampling toward the opinion prevalent in the social class living in the neighborhood.


Figure 1: Possible sources of bias occurring in the selection of a sample from a population.
In social and economic sciences, extracting random samples typically requires a sampling frame such as the list of the units of the whole population, or some auxiliary information on some key characteristics of the target population to be sampled. For instance, conducting a study about primary schools in a certain country requires obtaining a list of all schools in the country, from which a sample can be extracted. However, using a sampling frame does not necessarily prevent sampling bias. For example, one may fail to correctly determine the target population or use outdated and incomplete information, thereby excluding sections of the target population. Furthermore, even when the sampling frame is selected properly, sampling bias can arise from non-responsive sampling units (e.g. certain classes of subjects might be more likely to refuse to participate, or may be harder to contact etc.) Non-responses are particularly likely to cause bias whenever the reason of non-response is related to the phenomenon under study. Figure 1 illustrates how the mismatches between sampling frame and target population, as well as non-responses, could bias the sample.
In an unbiased sample, differences between the samples taken from a random variable and its true distribution, or differences between the samples of units from a population and the entire population they represent, should result only from chance. If their differences are not only due to chance, then there is a sampling bias. Sampling bias often arises because certain values of the variable are systematically under-represented or over-represented with respect to the true distribution of the variable (like in our opinion poll example above). Because of its consistent nature, sampling bias leads to a systematic distortion of the estimate of the sampled probability distribution. This distortion cannot be eliminated by increasing the number of data samples and must be corrected for by means of appropriate techniques, some of which are discussed below. In other words, polling an additional 1000 white college students will not improve the predictive power of our opinion poll, but polling 1000 individuals chosen at random from the electoral roll would. Obviously, a biased sample may cause problems in the measure of probability functionals (e.g., the variance or the entropy of the distribution), since any statistics computed from that sample has the potential to be consistently erroneous.
Contents
[hide]
1 Causes of sampling bias
2 Correction and reduction of sampling bias
3 Sampling bias, sampling error, bias of probability function, and limited sampling bias
4 The effect of limited sampling on the determination of statistical and causal relationships
5 Sampling bias in neuroscience
6 References
7 External links
8 See also
Causes of sampling bias
A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Sampling bias is also particularly prominent whenever researchers adopt sampling strategies based on judgment or convenience, in which the criterion used to select samples is somehow related to the variables of interest. For example, referring again to the opinion poll example, an academic researcher collecting opinion data may choose, because of convenience, to collect opinions mostly from college students because they happen to live nearby, and this will further bias the sampling toward the opinion prevalent in the social class living in the neighborhood.


Figure 1: Possible sources of bias occurring in the selection of a sample from a population.
In social and economic sciences, extracting random samples typically requires a sampling frame such as the list of the units of the whole population, or some auxiliary information on some key characteristics of the target population to be sampled. For instance, conducting a study about primary schools in a certain country requires obtaining a list of all schools in the country, from which a sample can be extracted. However, using a sampling frame does not necessarily prevent sampling bias. For example, one may fail to correctly determine the target population or use outdated and incomplete information, thereby excluding sections of the target population. Furthermore, even when the sampling frame is selected properly, sampling bias can arise from non-responsive sampling units (e.g. certain classes of subjects might be more likely to refuse to participate, or may be harder to contact etc.) Non-responses are particularly likely to cause bias whenever the reason of non-response is related to the phenomenon under study. Figure 1 illustrates how the mismatches between sampling frame and target population, as well as non-responses, could bias the sample.
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There is a possibility of bias element in deliberate sampling. The reason behind the same is as follows:
Explanation: It refers to the non probability sampling where the sample is selected on the basis of the purpose of the study. It is also known as purposive sampling. Under this method, all the sample units are not given equal chance of being selected as a sample.
Example: A teacher wants to take feedback of her teaching and to show positive results, only intelligent students are being chosen as sample for the same.
Reason: The reason behind the presence of bias element is that this is the non probability sampling in which all units are not given same preference of selecting or part of the research. Samples are being selected on the basis of the objective of the study.
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