The Goodness-of-Fit Test Calculator is an interactive and data-driven tool designed to help analysts, researchers, and decision-makers evaluate how well observed data fits an expected distribution. This tool allows users to input observed frequencies and expected probabilities or frequencies to perform statistical tests such as the Chi-Square Goodness-of-Fit test. It provides actionable insights into whether the observed data aligns with the expected distribution and helps stakeholders make informed decisions.

Key Features:

1. Dynamic Input Fields: Users can input observed frequencies and expected probabilities or frequencies for up to five categories.
2. Statistical Test: Automatically calculate the Chi-Square statistic, degrees of freedom, and p-value to determine goodness-of-fit.
3. Interactive Charts: Visualize observed vs. expected frequencies using bar charts for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in observed or expected values affect the test results.
5. PDF Export Functionality: Generate downloadable reports summarizing test results for presentations or sharing with stakeholders.
6. Modern and Stylish Design: A sleek interface with vibrant colors, animations, and clear typography to enhance user engagement.
7. Fully Responsive: Optimized for all devices, ensuring seamless functionality on desktops, tablets, and mobiles.
8. Self-Contained Container: The tool operates within its own container, ensuring no interference with the page header or footer.

Use Cases:

• Researchers testing whether survey responses follow a hypothesized distribution.
• Quality control analysts verifying whether production outputs match expected proportions.
• Business analysts assessing whether customer behavior aligns with predicted trends.

How It Works:
Users input observed frequencies and expected probabilities or frequencies for up to five categories. The tool calculates the Chi-Square statistic, degrees of freedom, and p-value to determine whether the observed data fits the expected distribution. Results are displayed in a table and visualized using interactive charts. Users can customize inputs dynamically, analyze scenarios, and download detailed reports in PDF format for further analysis or sharing with stakeholders.