Statistical Sampling Tool
This tool is built with pandas.DataFrame.sample, numpy.random.choice, and numpy.random.randint functions to help you sample data from a dataset. You can select and configure a sampling method to generate a sample from your data. The tool provides summary statistics and visual comparisons between the original and sampled data. You can also export the sampled data to a CSV file.
Tool
Step 1: Import Your Data
Upload your dataset or use our sample data
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Statistical Sampling Methods
Statistical sampling is a process of selecting a subset of individuals from a population to estimate characteristics of the whole population. It's widely used in research, quality control, auditing, and data science when analysis of an entire population is impractical. Here is a visual guide to common sampling methods:
Simple Random Sampling
Each element in the population has an equal chance of selection.
Stratified Sampling
Population divided into distinct groups (strata), then samples taken from each stratum.
Systematic Sampling
Elements selected at regular intervals after a random start.
Cluster Sampling
Population divided into clusters, and entire clusters are randomly selected.
How Cluster Sampling Works in Our Tool
Cluster sampling selects entire groups (clusters) rather than individual elements:
- The population is divided into clusters based on a categorical variable
- The algorithm calculates the average cluster size
- It determines how many clusters to sample to approximate the desired sample size
- Clusters are randomly selected without replacement
- All elements from the selected clusters are included in the final sample
Two-Stage Cluster Sampling
Clusters are randomly selected, then elements are sampled within each selected cluster. Basically, simple random sampling after cluster sampling.
Weighted Sampling
Elements selected with probability proportional to a weight value.
Bootstrap Sampling
Sampling with replacement to create multiple resamples from the original dataset.