{"version":"1.0","type":"rich","title":"Fitting data to a power law, computing statistics and testing against other distributions.","description":"This example tests whether donation data from six nonprofit organizations follows a power-law distribution. It uses the `powerlaw` Python package to fit each dataset, compute the scaling parameter alpha, and compare the power-law fit against lognormal, exponential, stretched exponential, and truncated power-law alternatives using likelihood-ratio tests. The code outputs a LaTeX table with basic statistics (mean, standard deviation, maximum), fit parameters, and significance levels for each institution and year, highlighting cases where the p-value is below 0.05.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-6878629?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}