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Sample Size Calculator

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Calculate the minimum sample size needed to estimate a proportion within a chosen margin of error and confidence level.

Neural Network Parameter & FLOPs Calculator

Estimates total trainable parameters and forward-pass FLOPs for a fully-connected (dense) network from its layer sizes.

Total parameters
235.15K (235,146)
FLOPs per sample (forward pass)
469.90K
Total FLOPs (batch of 1)
469.90K
LayerShapeParametersFLOPs (per sample)
1784256200,960401,664
225612832,89665,664
3128101,2902,570

FLOPs counted as 2 per multiply-add (industry convention). Only covers fully-connected layers — convolutional/attention layers have different FLOP formulas not modeled here.

About Sample Size Calculator

Sample Size Calculator

Computes the minimum sample size needed to estimate a population proportion within a specified margin of error, at a chosen confidence level, using the standard formula n = z²p(1-p)/e². If a finite population size is provided, applies the finite population correction to reduce the required sample size accordingly.

Choosing a sample size before running a survey or experiment is a common statistics task — too small a sample and your results are too noisy to trust; too large and you waste time and budget collecting unnecessary data. This calculator applies the standard formula used in survey methodology and A/B testing so you don't have to derive it by hand.

Typical use cases: sizing a customer survey before fielding it, planning a poll with a target margin of error, or determining how many users an A/B test needs per variant.

Adjust confidence level, margin of error, and estimated proportion to see how each affects the required sample size in real time.

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