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1. Sampling Techniques An Introduction to Methods of Selecting Study Participants Dr. Manika Agarwal, Ph.D. Assistant Prof. DITU

2. Scenario 1: "Imagine you are a health officer and you want to find out how many people in your city are vaccinated against a new disease? Visiting every house in the city would take months and cost a lot. Instead, you decide to select a smaller group of people — a sample — to represent the entire city. But how do you choose them? Randomly? From specific areas? In what number? This is where sampling techniques come into play."

3. Scenario 2: Health Camp Planning Suppose you are organizing a free health camp in a village. You want to know if anemia is common among women there. But you cannot test every woman — too costly and time-consuming. Instead, you decide to select a few women from different age groups and areas. How you select them — that’s sampling ! And choosing the right sampling method will decide how good your camp findings are."

4. Scenario 3: Clinical Drug Trials "Imagine you are part of a clinical trial for a new drug to treat high blood pressure. You can’t test the drug on every single patient with high blood pressure worldwide. Instead, you select a group of patients with certain characteristics — age, severity of hypertension, etc. The way you select these patients — sampling — directly impacts the effectiveness of the trial and the generalizability of the results."

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6. Exp: Simple Random Sampling Scenario: Patient Blood Pressure Study "Imagine you’re studying the effectiveness of a new drug on lowering blood pressure. You have a list of all the patients who visit your clinic. To make sure every patient has an equal chance of being selected, you randomly pick 100 patients from this list. This is Simple Random Sampling — everyone has the same chance to be part of the study."

7. Exp. Systematic Sampling Scenario: Hospital Infection Study "You want to assess the infection rates in patients who undergo a specific type of surgery in your hospital. You decide to select every 10th patient on a list of patients who underwent surgery over the last year. This is Systematic Sampling — you select patients at regular intervals, ensuring a wide distribution across time."

8. Types of Systematic Random Sampling: Linear Systematic Sampling: You select every kᵗʰ item from the population list without going back to the beginning. Example: If you pick every 10th name from a list of 1,000 people, you continue until you reach the end. Circular Systematic Sampling: When you reach the end of the list, you circle back to the start and continue picking until you have the desired sample size. Example: If your list has 100 names and you need 30 samples by picking every 4th name, you go back to the beginning once you reach the end.

9. Exp. Stratified Sampling Scenario: Cancer Treatment Effectiveness Study "You are studying how a new cancer treatment works in patients of different ages. Instead of randomly selecting patients, you divide them into age groups (strata) — say, 20-30 years, 31-50 years, and 51+ years. Then, you randomly select patients from each group. This is Stratified Sampling — it ensures that every age group is represented properly in your study.“ Exp: Selecting students from different strata- Batch 2024, 2023, 2022 & 2021.

10. Criteria of a Good Sample: Representativeness: Adequate Size: Homogeneity within Groups: Heterogeneity between Groups: Practicality: Minimization of Bias: Accuracy and Precision:

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