Determining sample size often feels like a technical step where you just pick a formula and move on. In reality, this decision has a big impact on the quality of a study. A sample that is too small can weaken the findings, while making it larger without clear reasoning does not automatically make the research better.
That is why discussing thesis sample size should not stop at the final number. What matters more is understanding the logic behind it: the population, the research objective, the sampling technique, and field constraints. Once those are clear, the sample size becomes much easier to defend academically.
Start by understanding the population and unit of analysis
Before calculating anything, you need to be clear about who or what is being studied. Is the population all students in one faculty, active customers within a certain period, or employees in a specific department? Sample size cannot be separated from how the population is defined.
You also need to define the unit of analysis. Some studies focus on individuals, some on organizations, and others on documents or transactions. If the population and unit of analysis are still vague, the sample size will usually be weak as well.
Sample size is not always based on one formula
Many students immediately look for the Slovin formula because it is commonly mentioned online. It is popular, but that does not mean it is always the best choice. In some studies, sample size can be determined through tables, methodological standards, instrument needs, or by adjusting to the analytical model being used.
In other words, formulas are only supporting tools. You still need to read them in context. A simple design with a clearly defined population may use one approach, while a more complex study with many indicators may require something different.
Adjust it to the sampling technique
A good sample size should also make sense in relation to the sampling technique. If you use random sampling, the population list and selection probability should be relatively clear. If you use purposive sampling, what matters more is whether the respondents fit the study criteria.
A common mistake here is having a large sample size on paper while the respondent selection process is inconsistent. The numbers may look convincing, but the research design becomes weak. So the focus should not only be how many respondents you have, but also how they are selected.
Consider the analytical needs from the start
Different analytical techniques require different amounts and structures of data. A simple descriptive study is not the same as a study that tests relationships between variables or builds a more complex model. The more demanding the analysis, the more careful the sampling decision needs to be.
That is why sample size should always be linked to what the data will be used for later. A number that seems sufficient at first may turn out to be too small when you reach validity testing, assumption testing, or the main analysis stage.
Common mistakes when determining thesis sample size
Several mistakes appear repeatedly in student research:
- choosing a formula only because friends used it,
- failing to define the population clearly,
- ignoring the sampling technique,
- setting respondent targets that are unrealistic in the field,
- and not matching the sample size to the planned analysis.
These issues may look small at first, but they often make the methodology chapter easier to question during supervision or defense. A sample-size decision should come from sound reasoning, not from copying a template.
How to write it in the methodology chapter
In a thesis, the explanation should be concise but clear. Ideally, you explain the population, the sampling technique, the basis for determining the sample, and the reason why that approach was chosen. That way, your supervisor can see that the number was not chosen carelessly.
If you use a formula, present it only as needed and explain the context. If the sample is determined by respondent criteria or analytical needs, explain the logic in a calm and academic tone.
If you are still unsure how to determine the most reasonable sample size for your thesis or dissertation, what you often need is not just a formula but guidance in reading the whole research design. At Bimbingan Informal, this process can be supported from methodology planning and sampling review to making sure the analysis stays aligned with the research objective.