Statistics
Sampling Distributions and Estimation
USD 5.00
Learning Objectives:
By the end of this course, learners will be able to:
- Differentiate between populations, samples, parameters, and statistics, and explain the role of sampling in statistical inference.
- Explain the concept of sampling distributions and describe how sampling variability affects statistical estimation.
- Apply the Central Limit Theorem to justify the use of normal probability models in statistical inference.
- Evaluate the properties of statistical estimators, including unbiasedness, consistency, efficiency, and sufficiency.
- Distinguish between the normal (z) distribution and the Student's t-distribution, and determine the appropriate distribution for statistical estimation.
- Compute and interpret margins of error and explain the factors that influence estimation precision.
- Assess the limitations of statistical estimation by identifying potential sources of sampling and measurement bias.