Calibrated for CPSP, PMDC, HEC & WHO Standards

SPSS Medical Sample Size & Feasibility Calculator

Quickly calculate the required sample size and statistical power for your clinical research, FCPS synopsis, dissertation, or randomized trial. Select your study design below to generate your sample size and copy your official CPSP synopsis justification paragraph instantly.

6 Medical Study Designs 1-Click CPSP Synopsis Generator 100% Free & Peer-Validated
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1

Select Medical Study Design

Choose your research methodology to load the exact WHO/Cochran formula

2

Statistical Parameters & Assumptions

Cross-Sectional / Prevalence Parameters

Alpha (α) error = 5% (two-tailed)
20%

From previous published literature / local reference study.

5%

Standard medical research precision is 5% (0.05) or 3% to 7%.

10%
Standard CPSP & IRB recommendation is 10% to prevent loss to follow-up bias.
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Comprehensive Guide to Medical Sample Size Determination

In medical research and postgraduate clinical training (such as FCPS, MCPS, MD, MS, and PhD dissertations), determining the appropriate sample size is a mandatory ethical and scientific requirement. Institutional Review Boards (IRB), the College of Physicians and Surgeons Pakistan (CPSP), and international indexed journals (PubMed, Scopus) strictly scrutinize the sample size calculation section of any synopsis or protocol before granting approval.

Why Under-Powered or Over-Sampled Studies Fail:

❌ Consequences of Under-Sized Sample:

An underpowered study increases Type II error (β), meaning real clinical differences between treatments or prevalence patterns will fail to achieve statistical significance ($p < 0.05$), wasting time, clinical effort, and patient participation.

⚠️ Consequences of Over-Sized Sample:

Enrolling excessively large numbers of patients exposes unnecessary subjects to experimental procedures, delays dissertation completion, and squanders financial resources without adding scientific benefit.

Official WHO & Biostatistical Formulas by Study Design

Study Design Primary Statistical Formula Required Key Parameters
Cross-Sectional (Prevalence) n = (Z1-α/22 × P(1-P)) / d2 Expected prevalence (P), Margin of error (d), 95% CI
RCT / Two Independent Means n = 2 × (Zα + Zβ)2 × σ2 / (μ1 - μ2)2 Mean difference (Δ), Standard deviation (σ), 80% Power
Case-Control (Odds Ratio) Fleiss / Kelsey formula with OR Control exposure (P2), Anticipated Odds Ratio (OR), Ratio (r)
Diagnostic Accuracy Test n = (Z2 × Sens × (1-Sens)) / (d2 × Prev) Sensitivity, Specificity, Precision (d), Disease Prevalence
Correlation Coefficient n = ((Zα + Zβ) / 0.5·ln((1+r)/(1-r)))2 + 3 Expected correlation (r), Power (80%), Alpha (0.05)

Understanding Key Statistical Parameters for Doctors

1. Alpha Error (α) & Confidence Level

Alpha represents the probability of a False Positive result (Type I error). In medical research, α is universally set to 0.05 (5%), corresponding to a 95% Confidence Level ($Z = 1.96$).

2. Statistical Power (1 - β)

Power is the probability that your study will correctly detect a genuine clinical effect if one truly exists. The accepted benchmark is 80% Power ($Z = 0.84$) or 90% Power ($Z = 1.28$).

3. Absolute Precision / Margin of Error (d)

The acceptable distance between the sample estimate and the true population value. In single proportion studies, precision is commonly taken as 5% (0.05). Narrower precision (e.g. 2%) dramatically increases sample size.

4. Dropout / Attrition Compensation

Patients may withdraw consent, experience adverse events, or get lost to follow-up. Adding a 10% to 15% attrition buffer ensures your final analyzed dataset maintains statistical validity.

Frequently Asked Questions (Sample Size & SPSS)

How do I write the sample size justification in my CPSP synopsis?

Your synopsis methodology must state: the software/formula used (e.g., WHO sample size calculator), the confidence level (95%), margin of error (e.g., 5%), reference prevalence/mean from a cited published paper, and the final calculated number adjusted for non-response. You can directly copy our auto-generated paragraph into Section 5 of your CPSP synopsis form.

What if there is no previous study on my topic?

If no prior local or international literature exists, biostatistical convention dictates using P = 50% (0.50). Because $P(1-P)$ is maximized at 0.50, this yields the most conservative (maximum safe) sample size of 384 patients at 95% CI and 5% precision. Alternatively, a pilot study of 20–30 patients can be conducted.

Can Medswrites help me with SPSS data entry, coding, and analysis?

Yes! Medswrites specializes in comprehensive SPSS statistical analysis for medical trainees. We perform variable definition, data cleaning, normality testing (Shapiro-Wilk), parametric/non-parametric tests (t-tests, Mann-Whitney, Chi-Square, ANOVA, Kruskal-Wallis), and regression modeling with publication-ready APA/Vancouver tables.

What is the difference between 80% power and 90% power?

80% power means there is an 80% chance of detecting a statistically significant difference if a real clinical difference exists (with a 20% Type II error risk). 90% power increases rigor by reducing Type II error to 10%, but requires approximately 30% more patients.

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