In the world of national statistics and market research, it is physically and financially impossible to talk to everyone. Whether the government is measuring the unemployment rate or a brand is testing a new product, they rely on the science of Sample Surveys. This unit is the study of how to select a small group of people that perfectly “mirrors” a massive population. For students, mastering the design and analysis of these surveys is the difference between producing reliable data and producing “garbage” statistics that lead to poor policy decisions.
Below is the exam paper download link
PDF Past Paper On Design And Analysis Of Sample Surveys For Revision
Above is the exam paper download link
To help you navigate the complexities of sampling frames, bias, and estimation, we have prepared a revision guide centered on the core logic found in recent examination papers.
What is the “Sampling Frame” and why is it critical?
A Sampling Frame is the actual list or map from which you draw your sample—for example, a telephone directory, a list of registered voters, or a database of employees. In an exam, you might be asked about “Coverage Errors.” If your sampling frame is outdated or incomplete (e.g., using a landline directory to survey youth), your survey will suffer from Undercoverage Bias, meaning the results won’t represent the true population.
How do we define ‘Simple Random Sampling’ (SRS)?
SRS is the “purest” form of probability sampling. Every individual in the population has an equal and independent chance of being selected. While it sounds simple, it can be difficult to implement in large, spread-out populations. In a past paper, you will often be asked to calculate the Sampling Variance for SRS and explain how “sampling with replacement” ($SRSWR$) differs from “sampling without replacement” ($SRSWOR$).
What is the advantage of ‘Stratified Random Sampling’?
If you know your population has distinct subgroups—like different age groups, religions, or income levels—you use Stratification. You divide the population into “Strata” and then take a random sample from each. This ensures that even small minority groups are represented in your data. In the analysis of experiments, stratification is used to reduce the standard error, making your estimates much more precise than a standard SRS.
How does ‘Cluster Sampling’ save costs?
In a country like Kenya, sending researchers to every single village is too expensive. In Cluster Sampling, you divide the population into “Clusters” (like counties or sub-locations), randomly pick a few clusters, and then survey everyone within those chosen clusters. While this saves massive amounts of money on travel, it usually has a higher “Design Effect” (sampling error) than stratified sampling because people in the same cluster tend to be similar to each other.
What is ‘Systematic Sampling’?
This is the “every $k^{th}$ person” method. You pick a random starting point on your list and then select every 10th or 100th person. It is incredibly easy to execute in the field. However, examiners will often warn you about Periodicity. If your list has a hidden pattern that matches your sampling interval (e.g., selecting every 7th day for a store traffic survey), your sample will be heavily biased.
Why is ‘Non-Response Bias’ a major threat?
You can have the most perfect mathematical design, but if 50% of the people you pick refuse to answer the survey, your data is compromised. Non-Response Bias happens when the people who don’t answer are fundamentally different from those who do. For example, if you survey people about their “free time” during work hours, you will only hear from unemployed people or those with flexible jobs, skewing your results.
What is the ‘Finite Population Correction’ (FPC)?
When you are sampling a significant portion of a small population (usually more than 5%), the standard formulas for variance become too large. We apply the FPC to “shrink” the standard error, acknowledging that as we sample more of a small group, our uncertainty decreases.
Conclusion
“Design and Analysis of Sample Surveys” is about the balance between mathematical precision and the messy reality of fieldwork. Success in your exams comes from knowing which sampling method fits a specific budget and population structure. The best way to build this intuition is to solve the calculation-heavy problems found in previous years’ papers.
To help you master the formulas for means, proportions, and totals, we have provided a link to the essential revision materials below.
Last updated on: March 24, 2026