This is part 2 of a 4 part series, covering bias in data collection: what bias is, who data bias can affect, the importance of awareness of data bias, and ways in which we (as analysts and consultants) can attempt to mitigate bias in the collection and analysis phases.
Why is it Important to Understand Bias?
Biased data can result in biased results, there is a saying of "Garbage in - Garbage out". When working with data to come to decisions, create statistical analysis, or produce machine learning models, the decisions and results will only be as good as the data they are trained on. If the data is biased, the models and algorithms will also be biased.
It is, therefore, important to be aware of biases in data. This awareness can ensure accurate decision-making, as biases in data can lead to inaccurate conclusions and flawed decision-making, particularly in areas such as public policy, healthcare, and business. Finally, awareness of biases in data can result in personal and professional growth, by being aware of biases in data, individuals can develop a greater understanding of their own biases and blind spots, and take steps to minimize their impact. This can promote personal and professional growth and increase the ability to make informed and equitable decisions.
On the side of the publisher, evidence of unmitigated bias can restrict trust in institutions such as government agencies or companies, that use data to inform decision-making. If individuals perceive that the data being used is biased or unreliable, they may be less likely to trust the decisions made based on that data. Bias can also reflect on the scientific integrity of the publication and undermine the credibility of the scientific community as a whole. By promoting rigorous and objective data collection and analysis, we can ensure that scientific research remains a trusted and reliable source of knowledge.
Furthermore, awareness of biases can ensure equity and fairness - by identifying and addressing biases in data, we can promote greater equity and fairness in decision-making and ensure that all groups are represented and considered. Misrepresentation in data can lead to unfair and inaccurate decisions that can affect many groups in differing ways. Section 3 explores the types of groups that unmitigated data bias can affect and can be read here.