<ol><li>Not including the number of trials the experiment / research took - given a small enough number of cases, random 'by chance' occurrences can happen.</li><li>Leaving out the Degree of Significance - the demanded level is 1% which means there are 99 chances out of 100 that have an apparent difference.</li><li>The Range - this figure tells you the deviation of the sample from the average that is given.</li></ol>Place little faith in an average or a graph or a trend when those important figures are missing

Method 3: Missing Figures

There are 3 common averages: mean, median and mode. In some cases, the result of all 3 is the same number but in most cases the numbers are very different! The Median (middle number) tells you more about the situation. On the left of this number is 50% of the sample on the right is the other 50% of the sample. Thus gives a good overview of what's happening as a whole. But marketers normally report using the mean (the sum of all numbers divided by the number of units in the sample).

Method 2:  Reporting The Wrong Average

<ol><li>Look for conscious bias - ask "Who says so?"</li><li>Ask yourself "How does HE know that?" What methods were used (if any) to acquire that information? </li><li>Does it make sense - use your own intellect to see through the cracks!</li></ol>

How To Talk To a Statistic

A QUICK READ 💙

<ul><li>Researchers can intentionally choose a group of people (or things) that have similar traits and thus 'prove' their point. </li><li>This is deceiving because a report based on sampling must use a representative sample - every name or thing in the whole group must have an equal chance. </li><li>Another famous method is choosing a very small sample. </li><li>Depending on the population size, samples sizes must be at least 30% of the population. </li></ul>

Method 1: Using Samples with bias

<ol><li>The Probable Error and The Standard Error - these tell you how accurately the sample presents the whole field (or population)</li><li>The Test of Significance (often calculated with the degree of significance)</li></ol>

More Figures to Look For 👀

Only when there is a substantial number of trials involved is the law of averages a useful description or prediction

" If you can't prove what you want to prove, demonstrate something else and pretend that they are the same thing "The general method is to pick two things that sound the same, but are not.

Method 5: Make Assumptions

The secret language of statistics is employed to sensationalise, inflate, confuse and oversimplify.

Averages, Relationships, Trends and Graphs are not always what they seem. There may be more in them than meets the eye, and there may be a good deal less

<ul><li class="ql-align-justify">The book is a quick read filled with fun graphics to help you grasp ideas. </li><li class="ql-align-justify">The author does a good job of covering a variety of topics in a small book.</li><li class="ql-align-justify">However, the book lacks proper explanation of statistic concepts thus if you didn't take a stats class in University you will struggle to understand the concepts. </li><li class="ql-align-justify">There are a lot of practical examples of how these concepts have been used. </li></ul>

Book Overview

To make things seem bigger than what they are, marketers can use these methods / tools<ul><li>The Gee Whiz Graph - basically adding more numbers on the graph to create an upward or downward trend that otherwise looked like a straight line.</li><li>Bar Graphs - not using the same size for the bars or objects used</li></ul>

Method 4: Exaggerating

Let's explore how marketers lie using statistics (👀 and how you can too)

If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.