Learning Objectives

By the end of this lesson, learners should be able to:

  • Define population and sample.
  • Explain probability and non-probability sampling.
  • Describe common sampling methods.
  • Explain sampling error.
  • Construct basic confidence intervals.

Population And Sample

  • Population: Entire group of interest.
  • Sample: Subset selected from the population.

Sampling saves time and cost.

Probability Sampling Methods

Simple Random Sampling

Every member has an equal chance of selection.

Stratified Sampling

Population divided into groups (strata) and sampled from each group.

Systematic Sampling

Select every kth item.

Cluster Sampling

Select entire groups or clusters.

Non-Probability Sampling

  • Convenience sampling,
  • Judgment sampling,
  • Quota sampling.

Useful in exploratory research but less representative.

Sampling Error

Difference between sample result and true population value.

Larger samples generally reduce sampling error.

Confidence Intervals

A confidence interval provides a range likely to contain the population parameter.

Example: Average customer spending is estimated at $ 2,500 ± $ 200.

Managers can make decisions while recognizing uncertainty.

Business Application

A retailer surveys 400 customers instead of all customers to estimate average spending. The confidence interval helps management plan inventory and promotions.

Learning Materials / Reference Materials

  • Levin & Rubin. Statistics for Management.
  • OpenIntro Statistics.
  • SurveyMonkey Sampling Guide.

Lesson Summary

Sampling allows analysts to draw conclusions about large populations efficiently, while confidence intervals quantify the uncertainty of those conclusions.

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