Bias and design play a significant role in how information is presented, especially in surveys. Survey bias can manifest in the way questions are written or presented. A specific kind of bias, called acquiescence bias, occurs when only one side of an issue is presented, leading respondents to typically agree with the presented side.
This tactic, often used in political polling, manipulates responses to fit a narrative. Political polling is a type of survey used to gauge voters’ opinions on who they plan to vote for. While newspapers have long asked such questions, formal opinion polls predicting election outcomes began in the early twentieth century.
Consider the 1936 United States presidential election, where the Literary Digest conducted a massive mail-in survey predicting Republican Alf Landon would win by a landslide against incumbent Democrat Franklin D. Roosevelt. Despite its previous successes, the poll failed spectacularly, predicting Landon’s victory while Roosevelt won decisively. This failure was due to sampling bias—using a skewed sample that didn’t accurately reflect the American electorate. The survey was conducted through a mail-in survey, which meant that only Americans who could afford postage and had access to mailboxes were able to participate. This excluded many poor and rural Americans who supported Roosevelt, leading to a misrepresentation of public opinion.
Similarly, in the 1948 presidential election, pollsters predicted Republican Thomas Dewey would easily defeat incumbent Democrat Harry S. Truman. The polls, relying heavily on telephone surveys, excluded lower-income and rural Americans without access to phones. Additionally, Truman supporters, fearing being labeled soft on communism, were reluctant to express their support, leading to another inaccurate prediction.
The failures of these polls highlighted the need for rigorous polling methods. Despite improvements in polling techniques, biases still creep in. The 2016 presidential election serves as a recent example, where many pre-election polls predicted a comfortable win for Democrat Hillary Clinton. However, Republican Donald Trump won, largely due to sampling bias and social desirability bias—Trump supporters hesitant to express their support publicly but voting for him privately.
These examples underscore a critical theme: despite the consequences of past errors and manipulations, we seem predisposed to repeat our historical mistakes. Understanding these biases and questioning the data presented can help us see beyond the manipulation.
Dive deeper into these themes and learn how to sharpen your critical thinking skills by reading more in Think Like a Black Sheep. Discover how to navigate through biases and uncover the truth hidden in plain sight. Start your journey today and see the world with clarity and purpose. Read more in Think Like a Black Sheep and unlock the power of critical thinking.
