Poor practice is catching up with science,1-3 manifesting in part in the failure of results to be reproducible and replicable.4-7 Various causes have been posited,1, 8 but we believe that poor statistical education and practice are symptoms of and contributors to problems in science as a whole.
Fitbit, Garmin and other consumer heart-rate monitors are increasingly being used in clinical trials. The problem is, they’re not always very accurate.
Puerto Rico is in bad shape. Its government is burdened by a debt of $70 billion, while two ferocious storms in September are estimated to have caused up to $95 billion in damage, deepening the island’s economic woes and upending daily life for many of its inhabitants. Residents could therefore be forgiven for caring little about the fate of the Puerto Rico Institute of Statistics (PRIS), whose executive director, Mario Marazzi, is engaged in a legal fight to secure its independence.
“Best fit” line is one of the misleading terms that confuse occasional users of statistics. It usually implies a line fitted by linear regression that minimises a function of the distances from the line to each data point (measured parallel to one axis, and squared to make the sum positive). But does this mean it is the best approximation or interpretation of the data?
In "The Shock of the Mean" (Significance, December 2017) I explained how, far from being a simple and obvious idea, the use of the arithmetic mean to summarise data had to be wrested from the minds of the mathematicians and scientists of the eighteenth century. When it finally caught on, largely due to the efforts the French mathematician and astronomer Adolphe Quetelet, it was in a form that was, and continues to be, deeply misleading.