Calculate confidence intervals with our free online statistical calculator. Perfect for research, data analysis, and determining margin of error with 90%, 95%, or 99% confidence levels.
Enter your sample data to calculate confidence intervals and margin of error
Follow these simple steps to calculate confidence intervals for your statistical data
Input your sample mean, sample size, and standard deviation from your dataset.
Select 90%, 95%, or 99% confidence level based on your analysis requirements.
View the confidence interval bounds and margin of error for your data.
Advanced statistical tools for accurate confidence interval calculations and analysis
Calculate intervals at 90%, 95%, and 99% confidence levels for different precision needs.
Automatically calculate margin of error and understand the precision of your estimates.
Built-in Z-score calculations for accurate statistical analysis and interpretation.
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Understanding different confidence levels and their applications in statistical analysis
| Confidence Level | Z-Score | Interpretation | Best Used For |
|---|---|---|---|
| 90% | 1.645 | 90% chance interval contains true value | Preliminary analysis, quality control |
| 95% | 1.96 | 95% chance interval contains true value | General research, standard analysis |
| 99% | 2.576 | 99% chance interval contains true value | Medical research, critical decisions |
Common questions about confidence intervals and statistical analysis
A confidence interval is a range of values that likely contains the true population parameter with a specified level of confidence (e.g., 95% confidence means we're 95% certain the true value lies within this range).
The confidence level (90%, 95%, 99%) indicates how confident we are that the interval contains the true population parameter. Higher confidence levels create wider intervals.
The margin of error is the amount added and subtracted from the sample mean to create the confidence interval. The interval is mean ± margin of error.
Use 95% for most general purposes, 99% when you need higher certainty (medical research), and 90% when wider intervals are acceptable for preliminary analysis.
Generally, larger sample sizes (n > 30) provide more accurate confidence intervals. The larger the sample, the narrower and more precise the interval becomes.
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