What is Sample Size?
Sample size is the number of independent units, participants, cases, documents, observations, or clusters included in an analysis or qualitative inquiry after eligibility and data-quality rules are applied.
What good sample size looks like
A justified sample size follows from the study design and inferential goal, accounts for expected variability or information needs, anticipates attrition or unusable data, and distinguishes planned from achieved sample size.
- Unit of analysis and primary outcome or analytic purpose.
- Quantitative power/precision or qualitative information rationale.
- Design effects, subgroup needs, attrition, or nonresponse allowance.
- Planned, recruited, excluded, and analyzed counts.
A practical structure to follow
Use these elements as a decision checklist, not as a rigid formula. The exact wording should still fit the reader, context, and purpose.
- Unit of analysis and primary outcome or analytic purpose.
- Quantitative power/precision or qualitative information rationale.
- Design effects, subgroup needs, attrition, or nonresponse allowance.
- Planned, recruited, excluded, and analyzed counts.
How to write sample size step by step
- 1Identify the primary analysis or qualitative purpose before choosing a number.
- 2For quantitative studies, specify the expected effect or precision, significance level, power, and design assumptions where relevant.
- 3For qualitative studies, justify the range by information richness, case diversity, and stopping logic rather than statistical power.
- 4Inflate recruitment targets for anticipated attrition, missing data, or cluster design effects.
- 5Check whether planned subgroup comparisons require more cases than the overall estimate.
- 6Report the achieved analytic sample and reasons records were excluded.
8 Sample Size examples
Read the examples for structure and choices rather than copying surface wording. Notice what stays consistent and what changes with audience or purpose.
A two-group experiment plans 128 analyzed participants based on the primary outcome, then recruits 150 to allow for attrition.
A prevalence survey chooses a sample for a target margin of error and inflates it for expected nonresponse.
A cluster trial increases the individual-level estimate to account for correlation within sites.
A qualitative interview study begins with a planned range of 20–30 diverse participants and reviews whether new interviews continue to add relevant categories.
A document analysis includes every eligible annual report because the population is small enough for a census.
A usability study runs repeated small rounds because the goal is detecting interface problems, not estimating population prevalence.
Sample Size templates
Replace every bracketed field with situation-specific information. A template is a starting structure, not finished copy.
Quantitative rationale: The primary analysis requires [n] analyzed units for [power/precision assumptions]; we will recruit [n2] allowing [x%] loss.
Qualitative rationale: We will begin with [range] cases selected for [variation], then assess whether additional cases add materially new information.
Flow statement: screened [n] → eligible [n] → enrolled [n] → completed [n] → analyzed [n].
Common mistakes to avoid
- Choosing a round number with no connection to the analysis.
- Using a power calculation for a secondary outcome while presenting it as justification for the primary study.
- Treating qualitative saturation as an automatic number that applies across all designs.
- Reporting recruited participants as the sample analyzed when exclusions occurred later.
Final revision checklist
- Does the opening make the purpose clear quickly?
- Is every important claim, detail, or example doing a distinct job?
- Could a reader misunderstand any pronoun, transition, time reference, or instruction?
- Is the tone appropriate for the relationship and situation?
- Can you remove repetition without removing necessary context?
- If the writing contains factual claims, names, dates, quotations, or citations, have you verified them independently?
Questions about Sample Size
What is Sample Size?
Sample size is the number of independent units, participants, cases, documents, observations, or clusters included in an analysis or qualitative inquiry after eligibility and data-quality rules are applied.
What makes Sample Size effective?
A justified sample size follows from the study design and inferential goal, accounts for expected variability or information needs, anticipates attrition or unusable data, and distinguishes planned from achieved sample size.
How do I write Sample Size?
Start with the purpose and reader, then work through the structure in order. Draft for meaning first, check the examples for pattern, and do a final revision for clarity, accuracy, tone, and unnecessary repetition.
What should I avoid when writing Sample Size?
Choosing a round number with no connection to the analysis. Using a power calculation for a secondary outcome while presenting it as justification for the primary study. Treating qualitative saturation as an automatic number that applies across all designs.