Selection bias

1. Selection bias

Welcome. Data bias often stems from selection bias during data collection.

2. What is selection bias?

Selection bias is the bias introduced when the data for analysis is selected in a way that systematically favors certain individuals, groups, or characteristics. Consequently, this selection fails to ensure that the sample obtained is representative of the population intended to be analyzed. This can lead to skewed insights and compromise the generalizability of findings. Selection bias can be broken down into various bias types that describe how these biases originate. Let's delve into the five most common types.

3. 1. Sampling bias

One prevalent form is sampling bias. It occurs when the sampling method is not fair or random. It is important to address the term “sampling method” here. Essentially sampling bias originates from the approach we choose to obtain our sample which can make it hard or impossible to apply the findings to the whole population. For example, if an e-commerce platform analyzes customer satisfaction using a sampling method called convenience sampling, where easily accessible individuals such as those who participate in promotional events are overrepresented, the findings may not reflect the sentiments of the entire customer base.

4. 2. Undercoverage bias

Now consider another example where a market research study targeting online consumers, excludes individuals without internet access. This leads to findings that inaccurately represent the preferences of the entire consumer base. This is what we call undercoverage bias. Undercoverage bias highlights the inadequate representation of certain groups within the chosen sample. This occurs when we fail to include everyone that should be represented, leaving out certain groups from the analysis. While undercoverage bias is often considered similar to sampling bias because it involves issues with the sampling process, it is distinguished by its focus on the representation of specific groups rather than the randomness or fairness of the sampling method itself.

5. 3. Non-response bias

Next, we have the non-response bias. This bias arises when individuals who choose not to participate in a survey or study differ systematically from those who do participate. In a survey assessing employee satisfaction, non-response bias can occur if dissatisfied employees are less likely to participate, leading to an overly optimistic view of employee morale.

6. 4. Self-selection bias

While non-response bias specifically addresses those who do not respond, self-selection bias focuses on those who choose to participate. Self-selection bias is also called participation bias and it occurs when individuals choose to participate in a study or provide feedback. For example, if customers self-select to participate in a satisfaction survey, their views may not represent the broader customer base, especially if they have extreme opinions, skewing the overall perception.

7. 5. Survivorship bias

The fifth type is called survivorship bias. It occurs when only successful entities are included in the analysis, neglecting those that failed or did not achieve success. For instance, analyzing successful product launches without considering the ones that failed may lead to biased insights, overlooking critical factors that contribute to failure.

8. Creating a cohesive understanding

Understanding these various forms of selection bias is crucial for analysts and decision-makers. It's not uncommon for multiple biases to interact, complicating analyses. For example, a customer satisfaction survey may exhibit both self-selection bias and non-response bias.

9. Let's practice!

Now that we've explored selection bias, head over to the exercises to reinforce your understanding before moving forward in our journey to conquer data bias!

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