BioScale Research Manual
A guide to help researchers choose the correct Sample Size Calculator based on their study design and data type.
Phase 1: Quantitative Data (Means/Averages) 📈
Scenario 1: Estimating a Single Average
Goal: Determine the average value (μ) of a variable (e.g., blood pressure, test score) in one population. No comparison is made.
Example: Planning a study to estimate the average daily caloric intake of students at a university.
Go to Mean Estimation CalculatorScenario 2: Comparing Two Independent Groups
Goal: Compare the average outcome between two separate, non-related groups (e.g., two different treatment arms).
Example: Comparing the average recovery time (in days) between patients treated with Drug A and patients treated with a placebo.
Go to Independent Means CalculatorScenario 3: Comparing the Same Group (Paired Data)
Goal: Compare the average difference in outcome when the same subjects are measured under two different conditions (e.g., before and after an intervention).
Example: Measuring the anxiety score of participants before and after a meditation course to see if the course caused a statistically significant average change.
Go to Paired Means CalculatorPhase 2: Qualitative Data (Proportions/Prevalence) ✅
Scenario 4: Estimating a Single Prevalence
Goal: Determine the required sample size to estimate the proportion or prevalence of an attribute in a single population with a specific confidence.
Example: Planning a survey to estimate the percentage of the urban population that supports a new public health policy.
Go to Proportion Estimation CalculatorScenario 5: Comparing Two Simple Proportions (Unmatched)
Goal: Compare the rate/proportion of an outcome between two unmatched, independent groups (standard RCT or Cohort study design).
Example: Comparing the success rate of a surgical procedure (Proportion 1) versus a non-surgical treatment (Proportion 2).
Go to Unmatched Cohort / RCT CalculatorScenario 6: Case-Control (Unmatched)
Goal: Determine sample size for an unmatched case-control study where you estimate the sample size based on an assumed Odds Ratio (OR) and control exposure rate.
Example: Finding the sample size needed to detect a relationship between exposure to a certain toxin (exposure) and a rare disease (outcome) using standard Case-Control methods.
Go to Unmatched Case-Control CalculatorScenario 7: Case-Control (Matched/Paired)
Goal: Determine sample size for a case-control study where you are matching each case to a control based on confounding factors (age, gender, etc.).
Example: Determining sample size for a study on a specific risk factor, where each patient with the condition is matched one-to-one with a healthy patient of the exact same age and smoking status.
Go to Matched Case-Control Calculator