Mitigating Selection Bias in Data Analysis: Strategies for Accurate Insights
Introduction
Selection bias in data science refers to the systematic error introduced in the sampling process, where the selection of data points is not random and does not represent the entire population. It occurs when certain individuals or groups are more likely to be included or excluded from the data, leading to a skewed or unrepresentative sample.
Selection bias can arise due to various factors, such as non-random sampling methods, self-selection by participants, incomplete data collection, or data exclusion based on certain criteria. It can distort the results and conclusions drawn from the data, as the sample may not accurately reflect the characteristics of the population being studied.
Understanding Selection Bias in Customer Feedback
Suppose a company wants to gather customer feedback on their new mobile application. They decide to place a feedback form within the app itself, allowing users to provide their opinions and suggestions. However, they only collect feedback from users who actively navigate to the “Feedback” section within the app.
In this case, selection bias arises because the feedback collected only represents a subset of users who proactively seek…
