GITNUXSOFTWARE REVIEW
Data Science AnalyticsTop 10 Best Sample Size Software of 2026
This roundup compares sample size software tools and shows where each fits for planning studies, with worked examples and decision tradeoffs for teams.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Search agents gather the source set and the review record, including transcriptions of video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
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WebPower is the best pick for research teams that need quick, test-specific sample size and achieved power planning without coding because its parameter forms update instantly together, whereas G*Power is the better alternative for desktop-based power work across common test families thanks to its design modules for clustered and repeated-measures settings.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
WebPower
Test-specific parameter forms that directly return sample size and achieved power together.
Best for: Fits when research teams need quick, test-specific power and sample size planning without coding.
G*Power
Editor pickDesign-specific modules for clustered and repeated-measures settings that ask for the correlation structure and design effect inputs directly.
Best for: Fits when teams need interactive, desktop-based power analysis across common statistical tests without coding.
ClinCalc Sample Size Calculator
Editor pickSingle-purpose sample-size calculators with guided forms and directly interpretable numeric outputs for protocol use.
Best for: Fits when teams need quick, consistent sample-size numbers for common tests.
Comparison Table
WebPower
academicBrowser-based power analysis tool for ANOVA, regression, mediation, and structural equation models.
Test-specific parameter forms that directly return sample size and achieved power together.
WebPower targets study planning tasks such as determining sample size for mean and proportion comparisons and for variance-related scenarios. Each calculator uses a structured input form that maps directly to assumptions like effect size magnitude and allocation structure. Computed outputs include both the required sample size and the achieved operating characteristics for the provided settings, which reduces back-and-forth between planners and analysts.
A key tradeoff is that WebPower remains a web calculator rather than an analysis scripting environment, so automation depends on manual reuse of parameter screens. WebPower fits best for pre-analysis planning inside research groups that need consistent, reviewable planning calculations without building custom code.
- +Instant calculator updates support rapid sensitivity checks
- +Test-specific input forms reduce assumption entry mistakes
- +Produces both required sample size and corresponding power
- +Browser workflow avoids local installs for basic planning
- –Automation needs manual parameter entry across runs
- –Advanced design extensions beyond common test families are limited
clinical trial methodologists
Mean endpoint power planning
Validated planning inputs and N
survey research teams
Proportion comparison sizing
Defensible margin of error
Show 1 more scenario
psychology study leads
Paired differences planning
Clear N for pilot to study
Researchers use paired comparison calculators to convert expected effects into sample size requirements.
Best for: Fits when research teams need quick, test-specific power and sample size planning without coding.
G*Power
academicFree statistical power analysis software for t-tests, F-tests, chi-square, and regression models.
Design-specific modules for clustered and repeated-measures settings that ask for the correlation structure and design effect inputs directly.
G*Power is a free, desktop sample size tool used for calculating power and minimum detectable effect across common test families. It supports a wide range of study designs such as t-tests, chi-square tests, ANOVA variants, and several repeated-measures and mediation-related workflows, with effect size inputs driving the results.
The interface is geared toward fast recomputation of scenarios and generates clear numeric outputs without requiring scripting. Output can be exported for reporting, which reduces manual transcription when iterating allocation ratios or effect size assumptions.
- +Broad built-in support for many hypothesis test families and design variants
- +Instant recalculation when effect size, alpha, and sample size inputs change
- +Exports numeric outputs to support report writing without retyping
- +Effect size driven workflow keeps assumptions explicit in the input panels
- –Limited automation surface for batch studies and parameter sweeps
- –Some advanced designs require careful manual setup of correlation and variance assumptions
- –No native API for integrating calculations into study planning systems
- –Workflow breadth can hide assumptions behind many separate dialog screens
Psychology researchers
Compute t-test power for group comparisons
Clear minimum detectable effect
Biostatistics students
Run repeated-measures ANOVA power scenarios
Faster power iteration
Show 1 more scenario
Clinical trial methodologists
Estimate sample size for chi-square endpoints
Protocol-ready enrollment estimate
Uses chi-square test settings to translate proportion differences into required enrollment.
Best for: Fits when teams need interactive, desktop-based power analysis across common statistical tests without coding.
ClinCalc Sample Size Calculator
vertical specialistFree online calculators for superiority, non-inferiority, and equivalence trial sample sizes.
Single-purpose sample-size calculators with guided forms and directly interpretable numeric outputs for protocol use.
ClinCalc Sample Size Calculator uses a form-based flow to collect analysis settings like the test type, confidence level, power, and effect size or event-rate assumptions. It then returns computed sample sizes in a format that can be reused directly in study planning documents, including the companion inputs needed to interpret results. Output is focused on single-scenario planning rather than building a full multi-arm design space.
A concrete tradeoff is the limited room for advanced designs and correlation-based terms, which makes cluster or repeated-measures planning harder than in dedicated research tooling. The strongest fit is when study teams need fast, consistent sample-size outputs for a small set of predefined hypotheses and simple allocation assumptions.
The workflow also supports iterative parameter changes, which helps when teams refine assumptions like expected rates or standardized effect sizes during protocol drafting. Export or downstream integration is not the centerpiece, so documentation-friendly copy and manual transfer often define the practical workflow.
- +Guided inputs minimize transcription errors in power calculations
- +Clear output ties sample size to the selected test and assumptions
- +Fast iteration supports assumption refinement during protocol drafting
- +Covers frequent planning cases like t tests and proportion studies
- –Limited support for complex design structures like correlation-heavy workflows
- –No workflow-first automation surface for batch or programmatic recalculation
- –Export and integration depend on manual copy into documents
- –Fewer extensibility paths than software built for research pipelines
Clinical research coordinators
Protocol planning for a t test
Draft protocol with defensible numbers
Biostatistics analysts
Baseline check for proportion studies
Validate planning before deeper modeling
Show 1 more scenario
Biomedical project managers
Iterate assumptions with minimal overhead
Converge on final recruitment target
Adjust effect and confidence inputs to see updated sample size requirements.
Best for: Fits when teams need quick, consistent sample-size numbers for common tests.
PASS
enterpriseDedicated sample size and power calculation software covering over 960 statistical scenarios.
Finite population correction tied to planning inputs, producing planning outputs that reflect bounded sampling frames.
PASS from ncss.com computes sample sizes for many common study tests and power-analysis scenarios with built-in calculators rather than spreadsheet formulas. The workflow centers on specifying the test type, effect size inputs, and design settings, then exporting results for documentation.
- +Built-in calculators for common tests with direct parameter-to-result mapping
- +Finite population correction and attrition adjustments for realistic planning
- +Repeatable output tables that support study protocol write-ups
- +Handles both standard and more specialized planning settings
- –Limited automation surface for batch power scans
- –Assumption management relies on manual input changes
- –Workflow stays calculator-centric with less integration depth
- –Exported artifacts often need formatting for final reports
Clinical research biostatisticians
Estimate enrollment from study assumptions
Protocol planning basis
Public health proposal writers
Plan study power for research designs
Assumption-based planning outputs
Show 2 more scenarios
Clinical operations teams
Stress-test dropout and accrual assumptions
Earlier feasibility risk signals
Tests feasibility sensitivity to accrual, dropout, eligibility, endpoint, and analysis assumptions during protocol development.
Clinical trial design teams
Compare alternative arm and timing scenarios
Evidence for protocol selection
Explores alternative treatment arms, endpoints, timing, allocation, and population assumptions before design lock.
Best for: Fits when teams need disciplined sample size planning for common tests and realistic design assumptions.
Stata Power and Sample Size
enterpriseBuilt-in power command for sample size and effect size calculation across hundreds of methods.
Power calculations executed as Stata commands so outputs remain reproducible within existing do-file reporting.
Stata Power and Sample Size targets teams that already model data in Stata and need power calculations that fit the same model assumptions used in analysis. It calculates sample size or power for standard hypothesis tests and two-sided or one-sided alternatives, using effect sizes and test specifications that map directly to Stata’s testing options. It also supports planning for repeated-measures style workflows through repeated-measures related inputs where applicable.
- +Tight workflow fit because calculations live in Stata do-files
- +Power outputs align with Stata test options and effect-size inputs
- +Supports iterative planning by recalculating across parameter changes
- +Reproducible outputs integrate with existing reporting scripts
- –Less convenient for teams that do not use Stata
- –Some advanced design needs require manual derivation of inputs
Biostatisticians using Stata
Plan two-sided hypothesis test sample sizes
Faster go-no-go planning
Clinical trial analysts
Re-run power after effect estimates update
Aligned protocol sample sizing
Show 1 more scenario
Regulated research teams
Embed power analysis in audit-ready scripts
Consistent documentation artifacts
Keep power planning steps versioned and reproducible alongside the same Stata analysis code.
Best for: Fits when a Stata shop needs scripted, reproducible power planning tied to test assumptions.
SAS Power and Sample Size
enterprisePROC POWER and PROC GLMPOWER modules within the SAS analytics suite for study planning.
SAS PROCs for power analysis generate reproducible SAS outputs that slot into production SAS pipelines.
SAS Power and Sample Size targets teams that need study power analysis inside the SAS analytics workflow. It generates power calculations for common parametric tests and supports study planning with configurable assumptions like variance inputs, allocation ratios, and attrition handling.
- +Fits SAS-based study planning workflows with consistent inputs and outputs
- +Supports many standard hypothesis tests with configurable design assumptions
- +Produces results as SAS outputs that can feed reporting and documentation
- +Handles complex planning parameters like allocation ratio and variance assumptions
- –Less worksheet-like than standalone power calculators for quick iteration
- –Workflow depends on SAS environment readiness and familiarity with SAS execution
- –GUI-driven exploration is limited compared with tools that center on form inputs
- –Advanced designs can require careful parameter mapping into SAS statements
Statistical programmers
Generate power analysis within SAS workflows
Power results in SAS artifacts
Biostatistics governance teams
Standardize assumptions across analyses
Repeatable assumption tracking
Show 1 more scenario
Clinical trial planners
Size studies with attrition handling
Enrollment that accounts for dropout
Plans around dropout by incorporating attrition handling options during power and sample size calculations.
Best for: Fits when SAS shops need repeatable power analysis integrated into statistical programming and reporting.
Cytel East
enterpriseAdaptive clinical trial design software with sample size re-estimation and group sequential methods.
Study-specific configuration that keeps sample size assumptions consistent across protocol drafts and analysis planning artifacts.
Cytel East targets teams that need study-specific sample size work tied to real protocol assumptions and regulatory review workflows. It supports power analysis for common designs and also handles complex real-world adjustments like attrition and design effects.
- +Handles protocol-driven adjustments like attrition and design effects
- +Fits repeatable study workflows where inputs are versioned across drafts
- +Supports a wide set of standard hypothesis test contexts for power work
- +Outputs align with downstream analysis planning documentation needs
- –Workflow setup can be heavier than spreadsheet-driven power checks
- –Less efficient for ad hoc exploratory calculations with shifting assumptions
Regulated clinical teams
Prepare power work for review cycles
Review-ready sample size rationale
Trial design analysts
Adjust sizing for attrition and design effects
More realistic enrollment targets
Show 1 more scenario
Study task managers
Manage sample size inputs as repeatable tasks
Less manual reconciliation
Coordinates sample size inputs and outputs across drafts so planning is repeatable rather than spreadsheet-based.
Best for: Fits when protocol teams need repeatable, audit-friendly sample size calculations across study iterations.
Minitab Power and Sample Size
enterpriseIntegrated module within Minitab Statistical Software for hypothesis test power analysis.
Power and Sample Size templates built to reuse the same assumptions across planning outputs inside the Minitab report workflow.
Minitab Power and Sample Size computes study sample sizes from test choices, effect-size inputs, and target operating characteristics, with results returned as tables and plots. It integrates into the Minitab workflow for exporting outputs into reports and for using the same numeric assumptions across analysis and documentation.
- +Direct planning workflow that ties inputs to power curves and sample-size tables
- +Scenario comparison reduces repeated recalculation when assumptions change
- +Outputs export cleanly into Minitab report artifacts for documentation
- +Good coverage of common parametric and categorical hypothesis tests
- –Limited automation surface compared with scripting-first tools
- –Less coverage for advanced designs like clustered or time-to-event planning
- –Workflow stays mostly interactive, which slows large batch sensitivity runs
- –Some planning use cases depend on model choices that may not match study software
Operations analytics teams
Compare allocation scenarios with power curves
Faster scenario comparison
Biostatistics teams
Iterate assumptions when variance shifts
Updated power estimates
Show 2 more scenarios
Research reporting coordinators
Export power outputs for documentation
Consistent documentation outputs
Integrates with the Minitab workflow so the same numeric assumptions can be exported into reports.
Clinical data scientists
Size parametric and categorical tests
Right-sized study targets
Computes sample sizes from test choices and effect-size inputs for common parametric and categorical analyses.
Best for: Fits when teams need repeatable, assumption-driven sample-size tables tied to Minitab reporting workflows.
Sealed Envelope Power Calculator
vertical specialistWeb-based sample size calculators for parallel, crossover, and cluster randomized trials.
A guided, worksheet style form that ties sample size and achieved power outputs to the same input set for faster protocol drafting.
Sealed Envelope Power Calculator computes sample sizes and power for common hypothesis tests with a web based, worksheet style workflow. Inputs are collected in a guided form so studies for proportions, means, and categorical outcomes can be sized without exporting to separate tools.
- +Web form flow reduces the need for statistical software setup
- +Covers common test families for planning single study arms
- +Outputs required sample size and achieved power in one pass
- +Allocation ratio inputs support unbalanced recruitment planning
- –Limited coverage for clustered or repeated measures designs
- –No visible automation surface for bulk parameter sweeps
- –Result export options stay centered on readout rather than model files
- –Advanced assumptions like finite population correction are not clearly modeled
Academic researchers
Size proportion studies without exporting tools
Sample size and power in one place
Social science project leads
Plan mean comparisons with worksheet inputs
Reusable calculation text
Show 1 more scenario
Protocol drafters
Carry allocation ratio into planning outputs
Protocol-aligned enrollment estimate
Includes design knobs like allocation ratio so results align with protocol assumptions.
Best for: Fits when small teams need quick, shareable sample size numbers for standard tests without scripting.
OpenEpi
vertical specialistOpen-source web tool for epidemiologic statistics including sample size for proportions, means, and rate ratios.
Built-in design effect and finite population adjustments let users account for sampling constraints directly within sample size inputs.
OpenEpi centralizes sample size planning in a browser workflow with calculator pages for frequent test types and effect size entry.
- +Form-driven calculators cover proportions, means, and chi-square style analyses
- +Cluster and finite population options reduce manual spreadsheet work
- +Outputs include both sample size and power for iterative planning
- +Web execution avoids local software setup for standard analyses
- –Limited automation for batch power studies across many parameter sets
- –No documented API surface for programmatic integration into pipelines
- –Fewer advanced designs than general-purpose statistical workflows
- –No native reproducible report export for audit-grade parameter traceability
Epidemiology teams
Power studies with clustering adjustments
Cluster-aware sample size
Public health survey planners
Apply finite population correction
More accurate enrollment under constraints
Show 1 more scenario
Biostatistics support staff
Compute sample sizes from standard assumptions
Protocol-consistent calculations
Runs browser-based calculators for frequent test types by entering effect size and other protocol-aligned assumptions.
Best for: Fits when teams need quick, web-based sample size planning for common study types with basic clustering constraints.
Conclusion
After evaluating 10 sample size software, WebPower stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Sample Size Software
How does G*Power handle clustered and repeated-measures power inputs compared with WebPower?
Which tool produces power-analysis outputs in the same scripting workflow as the statistical analysis environment?
When should a team prefer PASS over G*Power for finite sampling constraints?
What breaks if the study needs attrition adjustment baked into planning rather than applied later?
How can sample size calculations be migrated from worksheet-style use into reproducible study artifacts?
Which tool is better for batch-style scenario iteration with visible prior inputs during tuning?
How do ClinCalc and OpenEpi differ when teams need web-based form workflows for common test families?
What is the practical tradeoff between Minitab templates and standalone calculators like Sealed Envelope when assumptions change often?
Which tool handles study-task consistency better when multiple drafts and amendments must share the same sample size configuration?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Sample Size Software
We see major gaps in how sample size software turns your study inputs into usable plans. The biggest separators tend to be Data import reliability and Output audit trail, then how well Margin controls the final recommendation.
Input options and assumptions coverage
Why it matters
Input options and assumptions coverage determines whether the software can represent how analysts actually frame a study, not just toy examples. If your assumptions don’t map cleanly, sample size recommendations can become a guess.
How to check
In a demo, choose a real scenario and try entering typical design assumptions (baseline rate or mean, target difference, variability, and allocation if relevant). Then check whether the tool offers the same assumption forms your team would use, or forces you into unnatural fields.
Supported sample size calculation methods
Why it matters
Supported sample size calculation methods matters because different study outcomes use different math, so the “same” inputs can yield different plans. If a method you need is missing, you may end up exporting and reworking elsewhere.
How to check
Ask the vendor to show at least one workflow for your outcome type (binary rate vs continuous mean vs count). Confirm the tool labels the approach clearly and lets you select the method rather than auto-switching without explanation.
Data import and population handling
Why it matters
Data import and population handling matters when teams reuse prior work, including existing cohorts and historical rates. Bad mapping or hidden transforms can shift the effective inputs behind the scenes.
How to check
Provide a small sample input file (or paste values) and verify the tool preserves each field and unit. Then compare the software’s “effective” inputs shown in the results panel with what you entered.
Output audit trail and report export
Why it matters
Output audit trail matters when analysts need to explain why a sample size recommendation changed between iterations. Without an exportable audit trail, reviews turn into manual reconstruction.
How to check
Run the same scenario twice with one changed assumption and export the report. Check whether the export includes the input values used, the intermediate settings, and a clear statement of what changed.
Sensitivity and scenario planning features
Why it matters
Sensitivity and scenario planning features help teams understand how fragile the recommendation is before committing to recruitment. This is especially important when assumptions vary across teams or time.
How to check
In the demo, change one assumption (like baseline variability or expected rate) and use the tool’s scenario view. Verify it can present multiple scenarios side-by-side and clearly names the assumption that moved.
Automation workflow and API access
Why it matters
Automation workflow and API access matters when analysts need to run many scenarios consistently or integrate into existing analysis pipelines. Without an automation surface, “repeatable” work becomes manual.
How to check
Ask what automation exists beyond a single UI run: a scriptable interface, API, or batch mode. Then request a sample request/response or a documented export format your team could feed into another workflow.
Red flags
- Assumptions you enter don’t match what the report claims was used
- A required calculation approach is missing or auto-chosen without disclosure
- Exports omit key inputs, leaving no clear path to reproduce results
Fit by situation
- If you expect analysts to reuse assumptions from past studies, prioritize Data import and population handling.
- If your team runs multiple outcome types, prioritize Supported sample size calculation methods.
- If you need repeated scenario runs with review-ready documentation, prioritize Output audit trail and report export.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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