Pervasive violation of statistical assumptions in studies linking solar variability to climate

All statistical tools come with assumptions. Yet many scientists treat statistics like a collection of black-box methods without learning the assumptions. Here I illustrate this problem using dozens of studies that claim to show that solar variability is a dominant driver of climate. I find that linear regression approaches are widely misused among these studies. In particular, they often violate the assumption of ‘no autocorrelation’ of the time series used, though it is common for studies to violate several or all of the assumptions of linear regression. The misuse of statistical tools has been a common problem across all fields of science for decades. This presentation serves as an important cautionary tale for the Earth Sciences and highlights the need for better statistical education and for statistical software that automatically checks input data for assumptions.