Gender Bias in Data

Gender bias in data occurs when information used for decisions or analysis favors one gender over another, often reflecting stereotypes or incomplete information. This can happen if data collection methods overlook or exclude certain groups, leading to skewed results. For example, if a job-growth study mostly surveys men, it might underestimate opportunities for women. Such biases can reinforce societal inequalities, affecting areas like hiring, healthcare, and education. Recognizing and addressing gender bias ensures data more accurately represents all genders, leading to fairer decisions and policies.