Target Population vs. Sample: What's the Difference?
You’re writing Chapter Three of your thesis proposal. You feel confident—until you reach the sections on “target population” and “sample.” Suddenly, you’re staring at your screen wondering: Are these the same thing? Do I need both? What am I supposed to write?
You are not alone. This is one of the most common confusion points for Kenyan students at every level—from undergraduate at Kenyatta University to PhD candidates at the University of Nairobi.
Here is the simple truth: Your target population is everyone you want to learn about. Your sample is the smaller group you actually study.
Understanding this distinction is critical. Get it wrong, and your supervisor will question the entire foundation of your research methodology. Get it right, and you demonstrate that you understand how research actually works in the real world.
If you are feeling stuck and need professional help crafting your Chapter Three methodology section, Proposal Writers Kenya has expert writers ready to help you define your population, determine your sample size, and justify your sampling technique.
What Is a Target Population? (The Big Picture)
Your target population is the entire group of people, objects, or events that you want to draw conclusions about. Think of it as the whole forest, not just a few trees.
A simple analogy: Imagine you want to understand the fishing experience on Lake Victoria. Your target population would be all fishermen fishing on Lake Victoria. That is your complete group of interest.
In academic terms, a well-defined target population has three clear boundaries:
| Boundary | What It Means | Example |
|---|---|---|
| Geographic | Where are they? | Fishermen in Kisumu County, Kenya |
| Demographic | Who are they? | Male and female fishermen aged 18-60 |
| Time | When are you studying them? | Active fishermen during the 2024/2025 fishing season |
Example for a Kenyan thesis: If your research is about remote work productivity at Safaricom, your target population would be “all employees working remotely at Safaricom’s Nairobi headquarters during the 2024 calendar year.”
Most Kenyan students work with finite populations—populations with a countable number of elements, like “the 5,000 third-year business students at the University of Nairobi.” This is good because finite populations make sample size calculation straightforward.
What Is a Sample? (The Manageable Piece)
Your sample is the smaller, manageable group you actually select from your target population to participate in your study. You cannot realistically survey every fisherman on Lake Victoria—there are thousands. Instead, you survey a representative few hundred.
Why do we need samples? Four practical reasons:
Time constraints – You don’t have years to collect data
Cost constraints – You don’t have an unlimited budget
Accessibility constraints – You cannot physically reach everyone
Practicality – Studying everyone (a census) is rarely necessary
The goal of sampling is simple: to make accurate generalizations about the entire population based on studying only a portion of it.
Continuing the Safaricom example: If your target population is all 5,000 remote employees, your sample might be 370 employees selected to complete your questionnaire. You study these 370 and use their responses to draw conclusions about all 5,000.
The Key Differences at a Glance
| Aspect | Target Population | Sample |
|---|---|---|
| Definition | The entire group of interest | A subset selected from the population |
| Symbol | N (capital N) | n (lowercase n) |
| Size | Large (e.g., N = 5,000) | Smaller (e.g., n = 370) |
| Purpose | Defines who your findings apply to | Provides data to represent the population |
| Data collection | Often impossible (requires a census) | Always possible (requires a survey) |
| When you write it | Before sampling | After deciding your sampling technique |
How Many Respondents Do You Really Need?
Once you have defined your target population (N), you need to determine your sample size (n). You cannot just pick a number out of thin air—your supervisor will ask for justification.
The Yamane Formula (most common for Kenyan undergraduate and master’s theses):
n = N / (1 + N × e²)
Where:
n = your required sample size
N = your total target population
e = your margin of error (usually 0.05, or 5%)
Worked example: If your target population is N = 5,000 employees, with a 5% margin of error:
n = 5,000 / (1 + 5,000 × 0.05²)
n = 5,000 / (1 + 5,000 × 0.0025)
n = 5,000 / (1 + 12.5)
n = 5,000 / 13.5
n = 370.37 (rounded to 370 respondents)
Simple guidelines by study type:
| Study Type | Typical Sample Size |
|---|---|
| Quantitative survey | 30-500 (depending on population size) |
| Correlational study | 50-100 participants |
| Experimental study | 30 per group minimum |
| Qualitative interviews | 10-30 participants (until saturation) |
| Case study | 1-5 cases |
How to Write Both Sections in Your Proposal
Writing Your Target Population Section
Use this exact template:
“The target population for this study comprised [describe participants] in [geographic location]. The population included [specific inclusion criteria]. According to [source of data], the total population size was N = [number] .”
Example: “The target population for this study comprised all remote employees working at Safaricom’s Nairobi headquarters. The population included full-time employees who had been working remotely for at least six months. According to Safaricom’s human resources department (2024), the total population size was N = 5,000.”
Writing Your Sample and Sampling Technique Section
Use this exact template:
“From the target population of N = [number] , a sample of n = [number] respondents was selected using [name of sampling technique] . The sample size was determined using [Yamane formula/Krejcie & Morgan table] . [Explain how the sampling technique was applied in practice].”
Example: “From the target population of N = 5,000, a sample of n = 370 respondents was selected using stratified random sampling. The sample size was determined using the Yamane formula at a 95% confidence level and 5% margin of error. Employees were first stratified by department, then randomly selected from each stratum proportionally.”
Common Mistakes to Avoid
| Mistake | Why It’s Wrong | How to Fix It |
|---|---|---|
| Using “population” and “sample” interchangeably | Confuses your reader about who you actually studied | Use the terms precisely as defined above |
| Defining population too broadly (e.g., “all Kenyans”) | Impossible to sample properly | Narrow to a specific, reachable group |
| Choosing a sample size without justification | Looks arbitrary and unprofessional | Use Yamane formula or cite Krejcie & Morgan |
| Stating “random sampling” without explaining how | Vague and suspicious | Describe the exact randomization process |
Conclusion
Let us bring this back to the simple truth: Your target population (N) is everyone you want to learn about. Your sample (n) is the smaller group you actually study. The sample allows you to make generalizations about the entire population without the impossible task of studying every single person.
When you write your Chapter Three, be specific about your boundaries, be honest about your sampling method, and always justify your sample size. Your supervisor is looking for clarity and feasibility—give them both.
If you are still struggling to define your target population, calculate your sample size, or write your sampling technique section, you do not have to figure it out alone. At Proposal Writers Kenya , our expert academic writers specialize in crafting clear, methodology chapters that impress supervisors. Whether you need help with Chapter Three or your entire thesis proposal, reach out to us today for a free quote.