Gather effect size, variance, attrition, MDC/MDE, alpha, power, and design details to speed up power analysis and sample size calculations.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
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Detailed Reports
Comprehensive insights and recommendations
Sample Survey Items
Q1
Dropdown
What is the study area?
Biomed/Clinical
Public Health
Social Science
Education
Economics
Psychology/Behavioral
Engineering
Other
Q2
Multiple Choice
What is the primary outcome type?
Continuous
Binary/Proportion (0–1)
Count
Time-to-event (survival)
Ordinal
Other
Q3
Multiple Choice
Which effect metric will the power analysis use?
Mean difference
Standardized mean difference (Cohen's d)
Odds ratio
Risk ratio
Risk difference
Hazard ratio
Rate ratio
Correlation coefficient
Change-score difference
Q4
Numeric
Planned effect size (enter value in the chosen metric; e.g., 0.3 for d, 1.5 for a ratio)
Accepts a numeric value
Whole numbers only
Q5
Numeric
Minimal detectable change (MDC/MDE) considered meaningful (use the same metric/units as the planned effect)
Accepts a numeric value
Whole numbers only
Q6
Short Text
Unit for the effect (e.g., mmHg, points); enter 'standardized' if unitless
Max 100 chars
Q7
Multiple Choice
Assumed distribution for the primary outcome
Normal
Binomial
Poisson
Negative binomial
Log-normal
Exponential
Weibull
Q8
Numeric
Assumed standard deviation (if continuous outcome; in outcome units)
Accepts a numeric value
Whole numbers only
Q9
Numeric
Expected control-group event rate/proportion (if binary; enter 0–1)
Accepts a numeric value
Whole numbers only
Q10
Long Text
Source(s) for variance/event-rate assumptions (e.g., pilot data, literature, registry), including citations or links
Max 600 chars
Q11
Dropdown
Number of arms/groups
1 (single-group)
2
3
4 or more
Q12
Dropdown
Allocation ratio
1:1
2:1
1:2
1:1:1
Other
Q13
Numeric
Significance level (alpha; e.g., 0.05)
Accepts a numeric value
Whole numbers only
Q14
Numeric
Target power (e.g., 0.80)
Accepts a numeric value
Whole numbers only
Q15
Multiple Choice
Test sidedness
Two-sided
One-sided
Q16
Multiple Choice
Is the design clustered/cluster-randomized?
No
Yes
Q17
Numeric
Intra-class correlation (ICC), if clustered (enter 0–1)
Accepts a numeric value
Whole numbers only
Q18
Numeric
Average cluster size, if clustered
Accepts a numeric value
Whole numbers only
Q19
Multiple Choice
Are there repeated measures/longitudinal outcomes?
No
Yes
Q20
Numeric
Within-subject correlation (rho), if repeated measures (enter 0–1)
Accepts a numeric value
Whole numbers only
Q21
Numeric
Planned follow-up duration (in weeks)
Accepts a numeric value
Whole numbers only
Q22
Numeric
Expected attrition/loss-to-follow-up proportion over the analysis window (enter 0–1)
Accepts a numeric value
Whole numbers only
Q23
Dropdown
Attrition rate is expressed per:
Week
Month
Entire follow-up period
Q24
Multiple Choice
Planned handling of missing data (select all that apply)
Complete-case analysis
Multiple imputation
Maximum likelihood/mixed models
Inverse probability weighting
Last observation carried forward
Q25
Rating
How confident are you in these assumptions?
Scale: 10 (star)
Min: Not confidentMax: Very confident
Q26
Multiple Choice
What informed these estimates? (select all that apply)
Pilot data
Prior RCT
Observational dataset
Systematic review/meta-analysis
Registry/EMR
Expert judgment
Feasibility constraints
Q27
Long Text
Citations, datasets, or notes relevant to these assumptions
Max 600 chars
Q28
Multiple Choice
Attention check: To confirm you are reading carefully, please select "I am paying attention."
I am paying attention
I am not paying attention
Q29
Multiple Choice
What is your primary role/discipline?
Biostatistician
Epidemiologist
Clinical researcher
Social scientist
Data analyst
Student/trainee
Other
Q30
Numeric
How many years of experience do you have with study design or analysis?
Accepts a numeric value
Whole numbers only
Q31
Dropdown
Which region are you primarily based in?
Africa
Asia
Europe
North America
South America
Oceania
Middle East
Multiple/Other
Q32
Dropdown
What type of organization do you primarily work in?
University/Academic
Hospital/Health system
Government
Industry/Pharma
Nonprofit/NGO
Independent consultant
Other
Q33
Short Text
Country (optional)
Max 100 chars
Q34
Long Text
Any additional context, assumptions, or constraints we should consider?
Max 600 chars
Q35
Chat Message
Welcome! This brief survey collects assumptions needed to plan a power/sample size analysis. Please answer as precisely as you can; best estimates are fine.
Q36
AI Interview
AI Interview: 2 Follow-up Questions on your study assumptions
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q37
Chat Message
Thank you for completing the survey. Your responses will help tailor an appropriate power/sample size plan.
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