Random Sample Generator

Each member of the population has an equal probability of being selected. This is the most basic form of random sampling.

Use case: When the population is homogeneous and you want every member to have an equal chance of selection.

Example: Selecting 100 random customers from a database of 10,000 customers.

Select every kth element from the population after a random starting point. The interval k is calculated as: k = population_size / sample_size.

Use case: When you have an ordered list and want to ensure even coverage across the entire population.

Example: Selecting every 10th person from a list of 1,000 people to get a sample of 100.

Divide the population into homogeneous subgroups (strata) and randomly sample from each stratum proportionally.

Use case: When the population has distinct subgroups and you want to ensure each subgroup is adequately represented.

Example: Sampling from different age groups, ensuring each age group is represented proportionally in the sample.

• Sample Indices: Zero-based indices of selected elements (e.g., [0, 5, 12, ...])
• Sample Size: Actual number of samples generated
• Sampling Rate: Percentage of population sampled
• Distribution Chart: Visual representation of sample distribution across population

Using a random seed allows you to reproduce the same sample. This is useful for:

• Reproducible research and analysis
• Debugging and testing
• Sharing methods with colleagues who need the same sample

Note: The same seed with the same parameters will always produce the same sample.