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Tags: Analytics, Lang:en
Summary
If you are looking for a short beginners guide packed
with visual examples, this booklet is for you.
****Statistical significance is a way of determining if an
outcome occurred by random chance, or did something cause that
outcome to be different than the expected baseline.
Statistical significance calculations find their way into
scientific and engineering tests of all kinds, from medical
tests with control group and a testing group, to the analysis
of how strong a newly made batch of parts is. Those same
calculations are also used in investment decisions. This book goes through all the major types of statistical
significance calculations, and works through an example using
them, and explains when you would use that specific type
instead of one of the others. Just as importantly, this
book is loaded with visual examples of what exactly statistical
significance is, and the book doesn't assume that you have
prior in depth knowledge of statistics or that you regularly
use an advanced statistics software package. If you know
what an average is and can use Excel, this book will build the
rest of the knowledge, and do so in an intuitive way. For
instance did you know that In fact, you probably already know this key concept in
statistical significance, although you might not have made the
connection. The concept is this. Roll a single
die. Is any number more likely to come up than another
? No, they are all equally likely. Now roll 2
dice and take their sum. Suddenly the number 7 is the
most likely sum (which is why casinos win on it in
craps). The probability of the outcome of any
single die didn't change, but the probability of the outcome of
the average of all the dice rolled became more
predictable. If you keep increasing the number of dice
rolled, the outcome of the average gets more and more
predictable. This is the exact same effect that is at
the heart of all the statistical significance equations (and is
explained in more detail in the book) The book that you are looking at on Amazon right now is the
second revision of the book. Earlier I said that you
might have missed the intuitive connections to statistical
significance that you already knew. Well that is because
I missed them in the first release of this book. The
first release included examples for the major types of
statistical significance A Z-Test A 1 Sample T-Test A Paired T Test A 2 Sample T-Test with equal variance A 2 Sample T-test with unequal variance Descriptions of how to use a T-table and a Z-table And those examples were good for what they were, but were
frankly not significantly different than you could find in many
statistics textbooks or on Wikipedia. However this
revision builds on those examples, draws connections between
them, and most importantly explains concepts such as the normal
curve or statistical significance in a way that will stick with
you even if you don't remember the exact equation. If you are a visual learner and like to learn by example,
this intuitive booklet might be a good fit for you. Statistical
Significance is fascinating topic and likely touches your life
every single day. It is a very important tool that is used in
data analysis throughout a wide-range of industries - so take
an easy dive into the topic with this visual approach! **Hypothesis Testing & Statistical Significance
Statistical Significance Can Be Easily Understood By
Rolling A Few Dice?
You Are Looking At Revision 2 Of This Book