The Statistical Power Calculator is an interactive and data-driven tool designed to help researchers, analysts, and decision-makers determine the statistical power of a hypothesis test. Statistical power measures the probability of correctly rejecting a false null hypothesis (i.e., detecting an effect when it truly exists). This tool allows users to input parameters such as sample size, effect size, significance level (alpha), and type of test (e.g., t-test, chi-square test) to calculate the statistical power. It provides actionable insights into whether a study is adequately powered to detect meaningful effects.

Key Features:

1. Dynamic Input Fields: Users can input parameters such as sample size, effect size, significance level, and test type.
2. Statistical Power Calculation: Automatically compute the statistical power based on user inputs using established formulas or approximations.
3. Interactive Charts: Visualize the relationship between sample size, effect size, and power using line graphs or heatmaps for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in parameters affect statistical power.
5. PDF Export Functionality: Generate downloadable reports summarizing power analysis 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 designing experiments to ensure adequate sample sizes for detecting effects.
• Data scientists evaluating the feasibility of A/B tests or other hypothesis-driven analyses.
• Academics teaching statistical concepts like power, effect size, and Type II errors.

How It Works:
Users input parameters such as sample size, effect size, significance level (alpha), and test type (e.g., one-sample t-test, two-sample t-test, chi-square test). The tool calculates the statistical power and displays the result in a table. Outcomes are 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.