Statistics about telling the truth

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statistics about telling the truth

Lying Quotes (519 quotes)

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Published 12.12.2018

Jordan Peterson - Side Effects of Telling Lies

A free textbook for a one-semester, undergraduate statistics course.

Lies, Damned Lies, or Statistics: How to Tell the Truth with Statistics

If, as neuropsychologists, we think of the relationship between brain and behavior as the same as that between truth and reality, we must be equipped with statistical procedures that are coherent in terms of what we measure and what it represents. I believe that this necessary statistical procedure is effect size analysis, and without it, I believe that we fail to tell the truth, the whole truth, and nothing but the truth when describing our neuropsychological research. Accordingly, I review here the standard calculations of commonly employed effect sizes in two group designs and show how to adjust some familiar and perhaps not so familiar formulae using illustrative numerical examples. I also put forth an argument to adopt Cohen's measure as an expression of effect size based on its apropos to neuropsychological research. It is also argued that the interpretation of the magnitude of an effect size should depend on context, and not on pre-established heuristic benchmarks. It is noted, however, that effect sizes greater than 3.

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This file is very large 1. Depending on your Internet connection, the file may fail to open. If you are using a mobile device or your Internet connection is very slow, it may be safer to download the file instead of opening it directly in the browser. Figures often beguile me, particularly when I have the arranging of themmyself; in which case the remark attributed to Disraeli would often apply with justice and force:. We contend that statistics are not a type of lie, but rather, when used carefully, are an alternative to lying.

This allows for politicians to make claims that are not factually correct but are circulated by Twitter. But more importantly, the Citi team argues that the proliferation of digital media and the ease with which one can spread incorrect information are further pushing people to be less trusting of "the elites. Nowadays, people are increasingly likely to consider their friends, families, and inner circles as their "most trusted sources" of information. In fact, regular folks are more likely to trust "a person like themselves" over a non-governmental-organization rep, a financial or other industry analyst, or a CEO or government official, according to the Edelman Trust Barometer. The big takeaway here is that these two charts reflect regular people's frustration with and distrust of the perceived elites and large institutions whether a big bank or a political party. And that has led and could continue to lead to a rise in anti-establishment sentiment and political parties that break from the mainstream, which could eventually affect business and investment environments, according to Citi. But it will also be interesting to see any side effects of people listening only to people like themselves and whether that will lead even more wrong information to be spread.

At some point though during this past year especially it grew more difficult to distinguish a visualization snafu from bias and deliberate misinformation. Of course, lying with statistics has been a thing for a long time, but charts tend to spread far and wide these days. Before you know it, Leonardo DiCaprio spins a top on a table and no one cares if it falls or continues to rotate. This a guide to help you spot the visualization lies. Bar charts use length as their visual cue, so when someone makes the length shorter using the same data by truncating the value axis, the chart dramatizes differences.

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