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Biased Market Research Sample

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Biased Market Research Sample

During market research projects, it is possible to have biased market research sample. This is when a particular statistic in the sample does not accurately portray the true value within the target population.

Biased market research samples can appear in both qualitative and quantitative research.

An example of sampling bias might be that the average age of the sample is not reflective of the true average age of the members within the entire target population.

Though the most common ways that biased market research sample can occur are with averages and ratios. There are additional types of sampling bias, for example when companies rely too much on their existing customer database. These participants could provide overly favourable answers to questions compared to a random sample. This form of biased market research sample can be minimised by using stratified sampling techniques to gain a range of prospects, existing customers and lost customers. An example of sampling bias during qualitative research could be that when writing a report, the researcher may bias their output towards particularly memorable interviews. It is therefore vital that each response is given equal weight.

It is often not possible to eliminate sampling bias, therefore the market research project must be planned and organised in a way that will reduce potential bias to a minimum. It is also crucial that when the results are presented to clients, bias is identified and it is clearly highlighted in which direction the bias is.

Sample designs in market research projects often use random sampling. This can help guard against bias. Whereas other methods of sampling, such as selecting by judgement or simply out of convenience, do not offer the same protection against biased market research sample. Convenience sampling for example, has the major limitations of not being able to extrapolate from the sample to make conclusions about the whole target population. Judgement sampling is even more prone to bias, as the samples are frequently of smaller size and again, it is not possible to extrapolate from the sample.

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