Estimated Standard Error of Sample Proportion Calculator
The estimated standard error of sample proportion (SE) is a crucial statistical measure that quantifies the uncertainty of an estimate of a population proportion based on a sample. Understanding and calculating SE is vital for making informed decisions based on sample data.
- Enter your sample size in the ‘Sample Size’ field.
- Enter your sample proportion in the ‘Sample Proportion’ field.
- Click ‘Calculate’.
The formula for calculating the estimated standard error of sample proportion is:
SE = √[(p * (1 - p)) / n]
Where:
pis the sample proportion.nis the sample size.
Real-World Examples
Suppose a poll of 1000 voters finds that 550 support a new policy. The sample proportion is 0.55, and the sample size is 1000. The estimated standard error is:
SE = √[(0.55 * (1 - 0.55)) / 1000] ≈ 0.0288
Data & Statistics
| Sample Size | Sample Proportion | Estimated Standard Error |
|---|---|---|
| 100 | 0.5 | 0.0495 |
| 1000 | 0.5 | 0.0141 |
Expert Tips
- Larger sample sizes result in smaller standard errors, increasing the precision of the estimate.
- When the sample proportion is close to 0 or 1, the standard error is smaller, and the estimate is more precise.
Interactive FAQ
What is the difference between standard error and standard deviation?
Standard error (SE) is the standard deviation of the sampling distribution of a statistic, while standard deviation (SD) is a measure of the dispersion of a dataset.