https://github.com/priyanshgoantiya/confidence-interval-visualization

Confidence Interval Simulation
https://github.com/priyanshgoantiya/confidence-interval-visualization

matplotlib numpy plotly seaborn

Last synced: 3 months ago
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Confidence Interval Simulation

• Host: GitHub
• URL: https://github.com/priyanshgoantiya/confidence-interval-visualization
• Owner: priyanshgoantiya
• License: mit
• Created: 2025-04-10T08:41:40.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-10T08:45:27.000Z (over 1 year ago)
• Last Synced: 2025-06-04T00:30:31.304Z (about 1 year ago)
• Topics: matplotlib, numpy, plotly, seaborn
• Language: Python
• Homepage: https://vqpmrhmqzpu6y5twsbinnm.streamlit.app/
• Size: 7.81 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# confidence-interval-viz

Deployed-https://vqpmrhmqzpu6y5twsbinnm.streamlit.app/

## 🧪 Confidence Interval Simulation

This interactive simulation helps visualize how different types of confidence intervals behave over multiple random samples from a normal distribution.

You can explore:

- **Z-distribution using population standard deviation (σ)** — when population standard deviation is known.

- **Z-distribution using sample standard deviation (s)** — less common, but for comparison.

- **t-distribution using sample standard deviation (s)** — used when population standard deviation is unknown and sample size is small.

### 🔍 How it works:

- We simulate repeated sampling from a normal distribution.

- For each sample, we compute a confidence interval for the mean.

- If the interval captures the **true population mean**, it’s shown in **blue**; otherwise in **orange**.

- The red horizontal line represents the **true population mean**.

This tool demonstrates how often confidence intervals actually capture the population mean depending on:

- **Sample size**

- **Confidence level**

- **Distribution used (Z vs. T)**

Use the sliders in the sidebar to tweak parameters and understand how uncertainty and variability influence confidence intervals.