Selection bias is not just important in research. Businesses and people in their private lives also tend to select information in a biased manner or are presented with biased data due to selection. Cognitive biases contribute to us making mistakes in the selection process which then inevitably skews the results.
Such constantly recurring selection biases clearly show that we are not impartial and that impartiality requires a lot of effort. The following examples of selection bias illustrate the far-reaching consequences of sampling bias.
In the first example, a survey on general brand awareness for a health dietary supplement is to be conducted. If the survey is conducted in a gym, health food store, or organic supermarket, the products’ target audiences are being surveyed. This can be useful. However, the results of such market research must be interpreted with care because selection bias has already occurred. People who frequent gyms, health food stores, and organic supermarkets are generally more open to the effectiveness and benefits of health products. Therefore, it can be assumed that brand awareness is higher among this group of people because they were not surveyed in a neutral environment.
The second example of selection bias illustrates the far-reaching consequences of not making a truly random selection. When studying the economy, economic researchers should use a sample that is representative of all the companies in a country or region under study. If data selection is based on a register of limited and trading companies, for example, the final sample will exclude small businesses and successful freelancers (e.g. lawyers, doctors, architects), artists, and part-time workers.
It is an obvious example and experienced researchers are unlikely to make such a mistake. However, numerous smaller sampling biases can add up and quickly skew a country’s economic forecast.