Why is polling bias a concern in election forecasting, and what are common sources?

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Polling bias can distort forecasts by misrepresenting public opinion, which is a real danger when predicting election outcomes. Pollsters aim to measure the true views of a population, but several factors can tilt those measurements away from reality.

Common sources include sampling bias, where the group chosen for the poll isn’t representative of the whole population; nonresponse bias, where people who don’t respond differ in opinion from those who do; and mode effects, where the method of data collection (phone, online, in-person) influences how people answer. Additional influences include how questions are worded or ordered, coverage bias from gaps in the sampling frame, and timing effects as opinions shift over time.

These biases matter because they can make a poll's estimate too high or too low for a candidate's true level of support, and relying on biased results can lead to incorrect forecasts. Weather or other external factors don’t create these biases, and polls do not produce exact measures of opinion—their value comes from carefully addressing and adjusting for these biases.