Sample With The Built In Bias Summary

Statistics is the practice or science of collecting and analyzing information from numerical data. And in theory, people lie with statistics every day. When a person lies with statistics, they are unaware of or purposely misrepresenting statistical information and data, therefore making incorrect interpretations or assumptions about what a set of data infers. Without a particular amount of knowledge, statistics can be very misleading without certain background in knowing how to interpret it. A sample is defined as a small part or quantity intended to show what the whole should represent. Any statistic is based upon some type of sample, for it is impossible for a whole population to be tested. In the chapter “The Sample with the Built-in Bias”, Darrell Huff is saying that given any statistic, one should question the conditions of the sample that was taken. One should always assume

In the chapter “The Gee-Whiz Graph”, Huff describes how one can manipulate a graph with given data to express the results that one desires. Examples include changing the scale of a graph, excluding the measure of or not labeling the axis of a graph, therefore permitting the reader to make their own assumption about the data. In my opinion I believe that this is a negative aspect of the use of statistics, in the sense that all data should be a true and clear representation of given facts. When done in a ‘correct’ manner, graphs are very helpful in interpreting data; when done in a skeptical way, they can deceive. I believe that graphing data creatively provides plenty of space for creating false impressions, for there is no correct way to graph a set of data. When one plots information on a graph, it can either be a true and clear show of the facts, or a portion of it to make an entirely different point. Choice of ranges on graphs can have huge impact on