🎯 Practice Questions

1️⃣ What is the difference between a population and a sample?

2️⃣ What is the difference between a parameter and a statistic?

3️⃣ How does simple random sampling work?

4️⃣ When would stratified sampling be useful?

5️⃣ What is sampling bias?

🎯 Key Takeaways

✅ Population = entire group being studied.

✅ Sample = subset of the population.

✅ Parameter describes a population.

✅ Statistic describes a sample.

✅ Simple random sampling gives each member an equal chance.

✅ Systematic sampling selects at regular intervals.

✅ Stratified sampling ensures important subgroups are represented.

✅ Cluster sampling selects naturally occurring groups.

✅ Convenience sampling is easy but can introduce bias.

✅ A large sample is not necessarily a representative sample.

✅ Sampling is fundamental to statistical analysis and large-scale Data Science.

Understanding sampling will prepare you for the next major statistical topic: Hypothesis Testing, where you'll learn how to determine whether observed differences or relationships in data are statistically significant.

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