Sample Randomizer Tool Calculator

Randomizer Settings

Randomization mode

Text delimiter

Upload CSV or TXT

CSV headers may use item, category, and weight.

Population items

Use “item | category | weight” for structured entries. Category and weight are optional.

Sample size

Or sample percentage

Systematic interval

Use zero for an automatic interval.

Number of groups

Minimum group size

Maximum group size

Repeated draws

Random seed

Reuse the same seed and inputs to reproduce results.

Assignment categories

Always include

Exclude entries

Keep together

Keep apart

Remove duplicate entries

Sample with replacement

Use secure randomness without a seed

Formula Used

Simple random sampling gives each eligible item an equal chance. Without replacement, an item appears no more than once. With replacement, every draw uses the full population again.

P(item) = 1 ÷ N
Sampling fraction = selected items ÷ population × 100
Weighted probability = item weight ÷ total positive weight
Systematic interval k ≈ population ÷ desired sample size
Stratum allocation = sample size × stratum size ÷ population size

Fisher–Yates shuffling swaps each position with a random earlier position. Weighted selection uses cumulative positive weights. Seeded mode repeats the same pseudorandom sequence for identical settings.

How to Use This Calculator

Choose a randomization mode matching your task.
Paste entries or upload a CSV or TXT file.
Add optional categories and weights using pipe separators.
Set sample size, groups, replacement, rules, or a seed.
Select Randomize Now and review the generated result.
Copy, print, or download the result as CSV or PDF.

For reproducible work, save the exact population, settings, and displayed seed. Changing any input can change the resulting sample.

Example Data

Item	Category	Weight	Possible use
Alice	North	2	Higher weighted selection chance
Bob	South	1	Standard random entry
Cara	North	3	Stratified or weighted sampling
Diego	South	1	Group or category assignment

Frequently Asked Questions

Is the randomizer unbiased?

Uniform modes use equal-probability draws. Full-list mode uses Fisher–Yates shuffling. Input rules can intentionally change equal selection chances.

What does sampling with replacement mean?

Every selected item returns before the next draw. One item can appear multiple times. This suits simulations and repeated independent trials.

How do weighted samples work?

Each entry receives a nonnegative weight. Larger weights create proportionally larger selection chances. Zero-weight entries cannot win weighted draws.

What is a stratified sample?

Entries are divided using their category values. The tool samples proportionally within every category. This preserves the population’s category distribution.

Can I reproduce a result?

Enter and save a random seed. Reuse identical data and settings later. The same seeded random sequence will repeat.

How are random groups balanced?

Shuffled entries go into the smallest eligible group. Keep-together and keep-apart rules are considered. Strict rules can prevent perfectly equal groups.

Which CSV columns are supported?

Use item, category, and weight headers. Name, group, stratum, or probability are also recognized. Missing fields receive sensible default values.

Are uploaded entries stored?

The script processes uploaded files during the request. It does not intentionally save uploads permanently. Your hosting environment may keep server logs.

When should I use systematic sampling?

Use it for ordered lists needing spaced selections. A random starting point reduces positional bias. Avoid periodic lists matching the sampling interval.