Understanding Confirmation Bias and Selection Bias

Characteristics of Confirmation Bias, Selection Bias, or Both

Confirmation bias is the propensity to interpret information in a way that confirms pre-existing beliefs, while selection bias refers to non-random selection of individuals or data such that the sample isn't representative of the population. Certain behaviors, such as ignoring contradicting data or only choosing certain data for study, can signify these biases.

Explanation

In the realm of research and data gathering, two types of biases can crop up frequently: confirmation bias and selection bias. Confirmation bias refers to the tendency to focus on and interpret information in a way that confirms one's pre-existing beliefs or theories, whereas selection bias occurs when the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby ensuring that the sample obtained is not representative of the population intended to be analyzed.

Choosing only certain types of people, groups, or data for study - This is characteristic of selection bias because the object of study is intentionally chosen in such a way that it does not represent the full scope of the population.

Ignoring or dismissing data that contradicts one's own beliefs - This is a clear indication of confirmation bias, where one's own beliefs influence the interpretation and acceptance of data.

Forming a hypothesis and then searching for data to support it - This characteristic could potentially indicate both types of bias: confirmation bias when a person looks for data that supports their beliefs and ignores contradictory data; selection bias if the person knowingly selects only data sets that bolster their hypothesis.