May 17, 2024

Don warns media houses against misleading public with wrong statistics

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Don warns media houses against misleading public with wrong statistics

Ini Billie, Uyo

Professor of Optimal Experimental Design, Prof. Mfoniso Udofia Umoren has cautioned media professionals and organisations against misleading the public with wrong statistical data in their reportage. 

Speaking as the 89th inaugural lecturer at the University of Uyo on “Optimal Starting Point in Response Surface Exploration”, Umoren said some media organisations deliberately use inaccurate statistical graphs in their reportage that mislead the public into wrong conclusions on reported issues.

He advised that the use of statisticians in the compilations of such data would both checkmate wrong statistical reportage and enable the public to reach correct conclusions on such reported issues.

“Misuse of statistics is present everywhere and news outlets are not an exception.

“Practitioners in the advertising and the news industry are aware that American News Industry, Fox News has been under scrutiny several times over the years for showing misleading statistics graphs that seem to purposely portray a conclusion that is not accurate.

“The most recent case happened in September 2021, in which one of the few news broadcasts, Anchor Tucher Carlson displayed a graph showing that the number of Americans identifying as Christians had collapsed over the last decade which is a completely wrong conclusion,” he stated.

Prof. Umoren mentioned that the simplicity in the definition and purpose of

statistics has made many users fall into the temptation of abuse and misuse.

He listed some statistical abuse and misuse including bad samples, small samples, declaring precise numbers, absence of regularity conditions, faulty pillory, flawed correlation, and purposeful and selective bias among others.

He explained that statistics is a branch of mathematics that involves the use of quantified models, and makes representations and synopses for a given set of experimental data for real-life studies.

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