The Effect of Limited Sample Sizes on the Accuracy of the Estimated Scaling Parameter for Power-Law-Distributed Solar Data

TL;DR

This paper investigates how limited sample sizes affect the accuracy of estimating power-law exponents in solar data, highlighting the superiority of maximum likelihood methods over graphical approaches.

Contribution

It demonstrates the shortcomings of common estimation methods with small samples and advocates for maximum likelihood estimation in analyzing solar power-law data.

Findings

Graphical methods require large datasets for reliable exponent estimation.

Maximum likelihood estimator provides more accurate results with smaller samples.

Published solar data analyses may be affected by sample size limitations.

Abstract

Many natural processes exhibit power-law behavior. The power-law exponent is linked to the underlying physical process and therefore its precise value is of interest. With respect to the energy content of nanoflares, for example, a power-law exponent steeper than 2 is believed to be a necessary condition to solve the enigmatic coronal heating problem. Studying power-law distributions over several orders of magnitudes requires sufficient data and appropriate methodology. In this paper we demonstrate the shortcomings of some popular methods in solar physics that are applied to data of typical sample sizes. We use synthetic data to study the effect of the sample size on the performance of different estimation methods and show that vast amounts of data are needed to obtain a reliable result with graphical methods (where the power-law exponent is estimated by a linear fit on a log-transformed histogram of the data). We revisit published results on power laws for the angular width of solar coronal mass ejections and the radiative losses of nanoflares. We demonstrate the benefits of the maximum likelihood estimator and advocate its use.