Bite-Size Stats

Pirates, cats, Mexican lemons and North Carolina lawyers. Cheese consumption, margarine and drowning by falling out of fishing boats. This book has got it all. A roller coaster of a book in 8 witty chapters, this might just be the most entertaining statistics book you'll read this year. Did you know that pirates caused global warming, and that a statistical lie gave rise to one of the fastest growing religions on the planet? Probably not — you might have missed the memo that day. Did you also know that organic food is the real cause of autism, and that Mexican lemons are a major cause of deaths on American roads? They're true, honest — and this book has got the stats to prove it. In this eye-opening book, award winning statistician and author Lee Baker uncovers the key tricks of the trade used by politicians, corporations and other statistical conmen to deceive, hoodwink and otherwise dupe the unwary. Like how the ex-Chancellor of the Exchequer attempted to persuade us that 5 lots of 10 make 150, or how the President of the United States tried to convince us that 420,000 is a larger number than 782,000. Nice try boys, but we were awake that day! In his trademark sardonic style, the author reveals the secrets of how the statistical hustlers manipulate and misrepresent data for political or commercial gain — and often get away with it. Written as a layman's guide to lying, cheating and deceiving with data and statistics, there's not a dull page in sight! Discover the exciting world of statistical cheating and persuasive misdirection.

Sarah Palin, abortions, global warming and Usain Bolt. The CEO of Apple, 35 trillion gun deaths in 1995, Fox News and 193%. This book has got scandals galore! With 9 witty chapters taking you on a roller coaster tour of graphical lies, pictorial deceits and pie charts of mayhem, this might just be the most entertaining book about graphs you'll read this year. Did you know that between them, Sarah Palin, Mike Huckabee and Mitt Romney enjoyed a total of 193% support from Republican candidates in the 2012 US primaries? It must be true – it was on a pie chart broadcast on Fox News. Did you also know that the number 34 is smaller than 14, and zero is much bigger than 22? Honest, it's true, it was published in a respectable national newspaper after the 2017 UK General Election. There can't have been any kind of misdirection here because they were all shown on a pie chart. In this astonishing book, award winning statistician and author Lee Baker uncovers how politicians, the press, corporations and other statistical conmen use graphs and charts to deceive their unwitting audience. Like how a shocking, and yet seemingly innocuous statement as "Every year since 1950, the number of children gunned down has doubled", meant that there should have been at least 35 trillion gun deaths in 1995 alone, the year the quote was printed in a reputable journal. Or how an anti-abortion group made their point by trying to convince us all that 327,000 is actually a larger number than 935,573. Nice try, but no cigar – we weren't born yesterday. In his trademark sardonic style, the author reveals the secrets of how the statistical hustlers use graphs and charts to manipulate and misrepresent for political or commercial gain – and often get away with it. Written as a layman's guide to lying, cheating and deceiving with graphs, there's not a dull page in sight! And it's got elephants in it too…

Correlation Is Not Causation. You know it and I know it, and yet we are constantly having to be reminded of it because we can't seem to help but get it wrong. How many times have you heard someone really smart say something like 'wow, this correlation has a p-value of 0.000001 so A must be causing B…'? It's not our fault though – we're only human. We seek explanation for patterns and events that happen around us, and if something defies logic, we try to find a reason why it might make sense. If something doesn't add up, we make it up. OK, so if correlation does not necessarily imply causation, there must be a reason for that, and there must be something that is causing what we observe. That is what this book is all about. If we discover a correlation between a pair of variables there are five alternatives to one being the direct cause of the other, and we'll unmask all five in this book. Then, once we understand each of these alternatives, we'll formulate a plan to discover whether we have a direct causal link or whether there is some other explanation. Correlation Is Not Causation explains how to systematically test for the five most common correlation-causation pitfalls that even the pros fall into (occasionally). We'll learn to create strategies to analyse the data and interpret the results in a way that is easy to understand. Best of all, there is no technical or statistical jargon – it is written in plain English. It is packed with visually intuitive examples and makes no assumptions about your previous experience with correlations – in short, it is perfect for beginners!

Your correlation results are probably wrong. Sorry, but they are. You see, there is one really important thing to know about your correlations that mean that whatever results you get you can't be sure they are correct. This book fixes that. Correlation is a way of describing how a pair of variables change together as a result of their connection. In other words, if one of your variables changes, the other is likely to change too, and correlations measure by how much. Correlation analysis is one of the most used – and misunderstood – statistical techniques. Most correlation results are wrong, and for one very good reason. In this book we're going to understand just why this is, and learn how to fix it. Beginner's Guide to Correlation Analysis explains how to look at correlations with a focus on understanding the data, how to work with it, choose the right ways to analyse it, select the correct statistical tools and how to interpret the results in a way that is easy to understand. Best of all, there is no technical or statistical jargon – it is written in plain English. It is packed with visually intuitive examples and makes no assumptions about your previous experience with statistics or correlations – in short, it is perfect for beginners!

Data cleaning is a waste of time. If the data had been collected properly in the first place there wouldn't be any cleaning to do, and you wouldn't now be faced with the prospect of weeks of cleaning to get your dataset analysis-ready. Worse still, your boss won't understand why your analysis report isn't on his desk yet, a mere 48 hours after he's asked for it. Bless him, he doesn't understand – he thinks that cleaning data is just about clicking a few buttons in Excel and – ta da! – it's all done. Even a monkey can do that, right? And – for good reason – you won't get any help from statistics books either. Data is messy and cleaning it can be difficult, time-consuming and costly. Not to mention it's the least sexy thing you can do with a dataset. Yet you've still got to do it, because, well, someone has to… But it doesn't have to be so difficult. If you're organised and follow a few simple rules your data cleaning processes can be simple, fast and effective. Not to mention fun! Well, not fun exactly, just not quite as coma-inducing. Practical Data Cleaning (now in its 5th Edition!) explains the 19 most important tips about data cleaning with a focus on understanding your data, how to work with it, choose the right ways to analyse it, select the correct tools and how to interpret the results to get your data clean in double quick time. Best of all, there is no technical jargon – it is written in plain English and is perfect for beginners!

In “Multivariate Analysis—The Simplest Guide in the Universe”, award-winning statistician and author Lee Baker guides you through the building blocks of multivariate analysis towards discovering the relationships within your data. When searching for relationships in your data (associations and correlations), most results you'll get are wrong. In this book you're going to learn precisely why and learn how to make sure you get the correct results first time, every time. In this book, you will get the answers to questions such as: Why should I do multivariate analysis? How do I choose which type of multivariate test to use? How do I interpret the results of multivariate tests? What should I do when my univariate and multivariate results do not agree? In “Multivariate Analysis—The Simplest Guide in the Universe” you'll learn a holistic method of discovering the story of all the relationships in your data. The holistic method is about selecting the correct multivariate tests following your univariate analysis and using all the results in a single strategic framework to give you confidence that the story you discover is likely to be the true story of your data. “Multivariate Analysis—The Simplest Guide in the Universe” is written in plain English with a focus on understanding the data, how to work with it, choose the right ways to analyse it, select the correct statistical tools and how to interpret the results in a way that is easy to understand. It enables researchers of all backgrounds to understand and to evaluate critically the results of analyses that they are likely to encounter in their own research and in that of others. Best of all, it makes no assumptions about your previous experience with statistics, is packed with visually intuitive examples and is perfect for beginners!