Sampling Distribution

This topic introduces learners to the concept of sampling distributions and their role in statistical inference. Emphasis is placed on understanding variability in sample statistics, standard errors, and the basis for constructing confidence intervals.

Learning Outcomes:
The learner should be able to:

• Define and interpret sampling distributions for means and proportions.
• Standardise the sample mean and use the standardised variable to determine the probabilities.
• Differentiate between a population and sample.
• Learn how to compute the confidence intervals for the population mean when the population variance is known or unknown for large sample (n ≥ 30).
• Determine point and interval estimates of population parameters.
• Construct confidence intervals for population parameters.
• Apply confidence intervals and point estimation to solve real-world challenges.