Sampling Design

Bindika Pandey August 25, 2026

Sampling Design & Research Errors

The Brain-Friendly Revision Guide

🌿 MASTER IT IN 30 MINUTES

1. Core Definitions & Concepts

Population: All units or observations defined under the scope of research interest.
Sample & Sampling: A representative sample selects a subset possessing all characteristics of the population to save time and cost. Sampling enables researchers to draw generalizable conclusions. It also helps to test/build theory and assists in interpretation.
Sampling Design: The techniques or procedures adopted by a researcher to select items for the sample from the population.
🧠 BRAIN HOOK
POPULATION = ALL SAMPLE = SOME DESIGN = HOW TO SELECT

Think: ALL → SOME → HOW

Population = everyone in the research scope. Sample = a representative part. Sampling Design = the procedure used to choose that part.

2. Sampling Design & Process

Considerations for Sampling Design

FRAME → TECHNIQUE → SIZE → CONTACT Remember the 4 things before sampling: Where? How? How many? Contact how?
  1. The sampling frame
  2. Technique of selection of sample
  3. Sample size
  4. Selecting medium to contact

The 7-Step Sampling Process

DEFINE → FRAME → UNIT → METHOD → SIZE → PLAN → SELECT DFUMSPS — "Don't Forget Units, Methods, Size, Plan, Sample."
1. Define
Population
2. Specify
Sampling Frame
3. Specify
Sampling Unit
4. Select
Sampling Method
5. Determine
Sample Size (n)
6. Prepare
Sampling Plan
7. Select
Sample

3. Types of Sampling Techniques

1️⃣ Probability Sampling

“EVERYONE GETS A CHANCE” Every element has an equal chance of selection.
Technique Brain Hook Meaning
Simple Random 🎲 RANDOM Every element has an equal chance of being selected as a sample unit.
Systematic Random 🔢 FIXED INTERVAL Random first item, then select items at every fixed sampling interval.
Stratified 🏷️ STRATA Selection from overlapping homogeneous groups (strata) in the population.
Cluster 📦 CLUSTERS Identifies internally heterogeneous clusters within the population.
🧠 Probability Mnemonic
S = Simple S = Systematic S = Stratified C = Cluster

SSSC = "Some Students Study Carefully."

2️⃣ Non-Probability Sampling

“NO EQUAL CHANCE” Selection is based on pre-planned criteria.
Technique Brain Hook Meaning
Purposive / Judgmental 🧠 JUDGMENT Samples selected based purely on the researcher's judgment.
Quota 📊 SET RATE Population divided by traits/qualities and samples selected from each group at a set rate.
Convenience 🛋️ EASY Selection based on researcher convenience.
Self-Selecting 📢 VOLUNTEER Media solicits responses and respondents provide data spontaneously.
Snowball / Reference ❄️ ONE → MANY Initial fit respondents refer additional sample units; used for infinite or unfixed populations.
🧠 Non-Probability Mnemonic
P = Purposive Q = Quota C = Convenience S = Self S = Snowball

PQCSS = "Please Quit Choosing Simple Samples."

4. Sample Size & Determination

Determination Methods

C → S → T → S → O Census → Similar studies → Tables → Statistical methods → Other considerations
  1. Census for small populations
  2. Sample size from similar studies
  3. Published tables
  4. Statistical methods
  5. Other unique sample size considerations

6 General Principles of Sample Size

“MORE = MORE” More variation, precision, confidence or groups generally means a larger sample.
  1. Higher population dispersion/variation → larger sample.
  2. Higher desired estimation precision → larger sample.
  3. Smaller/narrower error range → larger sample.
  4. Higher confidence level → larger sample.
  5. More sub-groups → larger overall sample.
  6. If sample size exceeds 5% of population, it can be reduced without compromising reliability and validity.
🔥 EXAM MEMORY: Remember the first five as:

Variation ↑ → n ↑
Precision ↑ → n ↑
Error ↓ → n ↑
Confidence ↑ → n ↑
Groups ↑ → n ↑

The only special rule: >5% → sample can be reduced.

5. Research Errors & Minimization Strategies

A. Sampling Error

Definition: Statistically defined as the difference between population mean (μ) and sample mean (X̄).

Memory: Population − Sample = Sampling Error

6 Causes of Sampling Error

“FAULTY CHOICE MAKES SAMPLES FAIL” Think: Selection → Units → Statistics → Design → Size
  1. Faulty sample selection
  2. Selection of convenient units
  3. Faulty determination of sample units
  4. Improper choice of statistics
  5. Improper sample design
  6. Improper sample size

5 Minimization Methods

“SIZE → CHECK → UNBIASED → DESIGN → QUESTIONNAIRE”
  1. Increase sample size
  2. Cross check
  3. Unbiased sampling
  4. Appropriate sampling design
  5. Clear questionnaire

B. Non-Sampling Errors

Brain Hook: Sampling error is about the sample-selection problem. Non-sampling error covers problems in the research process itself.

7 Causes of Non-Sampling Error

DESIGN → COVERAGE → QUESTION → PROCESS → RESPONDENT → RESEARCHER → MEASURE
  1. Errors of poor sampling design
  2. Over and under coverage
  3. Misinterpretation of questions
  4. Processing errors
  5. Respondent-related errors
  6. Errors of researcher
  7. Measuring errors
🧠 7-Word Brain Chain

DESIGN → COVER → QUESTION → PROCESS → RESPONDENT → RESEARCHER → MEASURE

Imagine a researcher checking a survey: Design → Coverage → Question → Processing → Respondent → Researcher → Measurement.

9 Minimization Methods

“CHECK → QUESTIONNAIRE → PILOT → STANDARD → PEOPLE → INFO → TRAIN → EXPERT → CHECK”
  1. Effective checking of all processing/analysis steps.
  2. Careful preparation of questionnaire.
  3. Conducting a pilot survey.
  4. Fixing standard procedures.
  5. Use of competent manpower.
  6. Provide information (share experience/facts without revealing researcher weaknesses).
  7. Provide surveyor training (questionnaire completion, interviewing, social behavior, closing).
  8. Use of experts (in coding, recording, and decoding).
  9. Continuous checking and verification.

6. Sampling Error vs Non-Sampling Error

Sampling Error Non-Sampling Error
Difference between population mean (μ) and sample mean (X̄). Errors arising from problems throughout the research process.
Mainly connected with sample selection, design and size. Includes design, coverage, questions, processing, respondents, researcher and measurement.
Main causes: faulty selection, convenient units, wrong sample units, statistics, design and size. Main causes: poor design, coverage, question interpretation, processing, respondent, researcher and measurement.
🎯 ONE-LINE DIFFERENCE:

SAMPLING ERROR = “WHO DID WE SELECT?”
NON-SAMPLING ERROR = “WHAT WENT WRONG DURING THE RESEARCH?”

⏱️ Your 30-Minute Memory Plan

Don't read everything repeatedly. Use this sequence to force your brain to retrieve the information.

0–5 min
Core Definitions
5–10 min
Sampling Process
10–16 min
Sampling Types
16–20 min
Sample Size
20–25 min
Sampling Error
25–30 min
Non-Sampling Error
⚡ Final 60-Second Recall

Say these aloud without looking:

ALL → SOME → HOW
FRAME → TECHNIQUE → SIZE → CONTACT
DEFINE → FRAME → UNIT → METHOD → SIZE → PLAN → SELECT
PROBABILITY = EQUAL CHANCE
NON-PROBABILITY = NO EQUAL CHANCE
MORE = MORE SAMPLE
SAMPLING ERROR = SAMPLE SELECTION
NON-SAMPLING ERROR = RESEARCH PROCESS

🚀 Ultra-Quick Revision Sheet

Population = ALL

Sample = SOME

Sampling Design = HOW TO SELECT

Sampling Design: FRAME + TECHNIQUE + SIZE + CONTACT

Sampling Process: DEFINE → FRAME → UNIT → METHOD → SIZE → PLAN → SELECT

Probability: SIMPLE + SYSTEMATIC + STRATIFIED + CLUSTER

Non-Probability: PURPOSIVE + QUOTA + CONVENIENCE + SELF + SNOWBALL

Sample Size: MORE VARIATION / PRECISION / CONFIDENCE / GROUPS → MORE SAMPLE

Sampling Error: μ − X̄

Non-Sampling Error: DESIGN → COVERAGE → QUESTION → PROCESS → RESPONDENT → RESEARCHER → MEASURE

🔥 FINAL BRAIN FORMULA

WHO? → HOW? → HOW MANY? → WHAT WENT WRONG?

Population → Sampling Design → Sample Size → Research Errors
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