Being able to successfully utilize various evidence based research methods is essential for any research or policy analyst. There are a multitude of various techniques that can be leveraged depending on the situation ranging from structured analytic methods used in intelligence analysis, or statistical methods used in traditional quantitative data analysis. However, simply knowing how the use these techniques can be a dangerous thing as discussed in the book Wrong by David Freedman and which I summarize below.
Freedman presents the perspective that regardless of how prestigious a social scientist is, when analyzing, describing research, and general social problems, they can be as correct or flawed as the common layman. He attributes this to a variety of reasons including deception and flawed or skew perspective of those we consider experts. Overall, the anecdotes that Freedman uses all relates to varying degrees of cognitive bias. There are many kinds of cognitive bias and it is something that effects everyone, regardless of how well practiced in data science they are. The main types of bias that effect research can depend on data bias such as collection, confirmation, or data quality bias, or it can be skewed on how we form our hypotheses and beliefs, such as perspective, dis-confirmation, or anchoring bias. Freedman is able to relate these issues in anecdotal tales which leave the reader more aware of these issues, which can help the reader acknowledge and perceive the bias they have when examining a research subject.