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date: 12 December 2019

Abstract and Keywords

This chapter notes the important ways in which time series data are used in science, explains how trend lines are created and reported, chronicles ways in which they can be misused, documents human biases that lead to overvaluing endpoints in a trend, and outlines ways to minimize that bias. Specifically, this chapter defines trend lines and time series data and explain why and how they matter in science communication. A discussion on cognitive biases that influence interpretations of trend lines, such as endpoint bias and peak and end rule, recency bias, accessibility bias, and extrapolation bias, is offered as are communication tools scientists, journalists, and other science communicators can use to overcome these biases.

Keywords: trend lines, time series data, endpoint bias, recency bias, debiasing, peak and end rule, extrapolation bias

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