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How to Lie With Statistics by Darrell Huff - Book Report/Review Example

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The book “How to lie with statistics”, by Darrell huff was written in 1954 and describes the fallacies created using statistical inferences and materials. Huff aims at arming people with information on how to analyze all the research data provided and looking out for sampling errors and hidden biases. …
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How to Lie With Statistics by Darrell Huff
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Download file to see previous pages The book “How to lie with statistics”, by Darrell huff was written in 1954 and describes the fallacies created using statistical inferences and materials. Huff aims at arming people with information on how to analyze all the research data provided and looking out for sampling errors and hidden biases. Sampling Biases Sampling is the initial process in any research and aims at collecting data for analysis after formulation of the research topic or question. A complete enumeration of the target population is recommended as this increases the accuracy and precision. However, due to the cost implications and time, most researchers use a sample of the target population (Huff 10). Although a sample is supposed to represent all the characters of the population without any bias, this is rarely the case especially during organization marketing surveys and political opinion polls. For example, a sitting mayor seeking re-election in a certain city may pick a sample population to determine his popularity from his hometown county, where he got most of the votes during the last election. This sample population from his home town will give false results about his popularity and he will use these ratings to boost his campaign for re-election. Using this sample locks out people with a different opinion of the mayor from participating in the survey. Researches and surveys mostly use questionnaires or direct interviews in collecting data from the sample population. Interviews and questionnaires can be structured in a manner that misleads the respondent. For example, if Shell-BP is carrying out a survey on whether customers uses its lubricant, they can simply ask a respondent in a questionnaire or interview; “Have you ever used shell BP lubricant?” This question is open and will give misleading results, since it doesn’t ask how often the customer uses the lubricant and during which period they used it, as well as under what circumstances they used the lubricant. Sometimes, despite using the right wording during an interview or in a questionnaire, respondents might give inadequate information or lie. For example, in a social survey to find out the infidelity rate of married men in a certain population, an interviewer can ask a man how many times he has cheated during his marriage. Despite the assurance of confidentiality, some respondents might give false answers to such a question as they are scared of their partners finding out. Well Chosen Average According to Huff, one way that statistics are used in misleading the mass is by stating that a certain number is the average. In layman’s terms the meaning of average is taken as the mean which is calculated by adding up all data points and dividing the total by the number of the data points that were used. However, other measures of central tendency including the mode that refers to the most frequent data point, or the medium referring to the number where half of the data points are lower and the other half are higher, can also be used as average (Huff 45). Researchers do not reveal to the population whether they are using the mode, mean or medium as average in their surveys, although most people will assume that average automatically refers to the mean. Depending on what a person attempts to verify, they can use to use the term “average” in reporting their results whereas they have used either the mode, median or mean in proving their points. Little Figures That Are Not Here Darrell also illustrates the fallacies that can result from statistics if a small sample is chosen ...Download file to see next pagesRead More
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