Welcome to sampling considerations! You've learned to clean and analyze data, but here's a crucial question: does your data actually represent what you think it represents?
This determines whether your insights apply to your whole business or just a small slice.
Engagement Message
What's one group that might be missing from your company's customer feedback data?
Let's start with key terms. Your population is everyone you want to understand - all customers, all transactions, all website visitors.
Your sample is the subset you actually have data on. The goal is making your sample represent your population.
Engagement Message
If you want to understand all customers, but only have data on email subscribers, what's the problem?
A representative sample mirrors your population's key characteristics. If your customers are 60% women and 40% men, your sample should roughly match this split.
When samples don't match the population, you get biased results that don't apply broadly.
Engagement Message
Why might a survey of only high-spending customers give you biased insights?
Sampling bias happens when certain groups are systematically excluded or over-represented. Online surveys miss people without internet. Phone surveys miss people who don't answer unknown numbers.
Each data collection method has blind spots.
Engagement Message
What group might be missing from app usage data that only tracks logged-in users?
Common business sampling biases include survivor bias (only hearing from customers who stayed), convenience sampling (only easy-to-reach people), and volunteer bias (only people who choose to respond).
Each bias skews your understanding in predictable ways.
Engagement Message
How might "survivor bias" affect your understanding of product satisfaction?
