Inferential Statistics

🧪 What is Inferential Statistics?

Inferential statistics helps you make predictions or generalizations about a larger population based on a sample of data.

It answers the question:

"What can we infer about the population from this sample?"

🔑 Key Concepts:

• Population vs. Sample:
  • Population = the whole group you're interested in.
  • Sample = a smaller, manageable subset of the population.
• Estimation:
  • Using sample data to estimate population parameters (e.g. mean, proportion).
• Hypothesis Testing:
  • Testing assumptions (null vs. alternative hypothesis) to make conclusions.
• Confidence Intervals:
  • Range within which we believe the true population value lies, with a certain level of confidence (e.g., 95%).
• P-value:
  • Tells you the probability that the observed result is due to chance.

🧠 Real-World Examples:

• Politics: Predicting election results based on polls.
• Marketing: Testing if a new ad campaign performs better than the old one.
• Medicine: Determining if a new drug is more effective than the current treatment.
• Education: Using student samples to infer how all students are performing.

🆚 Descriptive vs. Inferential:

Would you like a simple example problem to see how inferential stats works in action?