[MINI] Selection Bias

Normalization

Normalization methods are essential for correcting selection bias in data collection. Kyle Polich explains that selection bias occurs when the sample does not accurately represent the target population, such as when more women than men are surveyed at a CVS, skewing the data. To address this, normalization techniques can be applied to weight responses according to the desired distribution, ensuring a balanced representation.

If you have, let's say, more than average or less than average of some Ethnographic group, you can weigh those responses proportionately to the representation to try and counteract the accidental selection bias.

--- Kyle Polich

These methods are crucial for obtaining reliable data that reflects the true characteristics of the population 1.

   

Business Use

In the business world, normalization techniques are vital for improving data reliability and decision-making. Kyle Polich highlights how businesses often face selection bias when surveying customers, as they typically only gather feedback from those who have made purchases. This bias can lead to skewed results that do not accurately reflect the entire customer base.

The results of any such survey would only be reflective of your customer base. You would not necessarily have a way to survey your customers who didn't make a purchase.

--- Kyle Polich

By applying normalization, businesses can adjust their data to better represent their entire market, leading to more informed and effective strategies 1.