People can make mistakes when they test a hypothesis with
statistical analysis. Specifically, they can make either Type I or
Type II errors.
As you analyze your own data and test hypotheses, understanding
the difference between Type I and Type II errors is extremely
important, because there's a risk of making each type of error in
every analysis, and the amount of risk is in your
if... Continue Reading
Welcome to the Hypothesis Test Casino! The featured game of the
house is roulette. But this is no ordinary game of
roulette. This is p-value roulette!
Here’s how it works: We have two roulette wheels, the Null wheel
and the Alternative wheel. Each wheel has 20 slots (instead of the
usual 37 or 38). You get to bet on one slot.
What happens if the ball lands in the slot you bet on? Well,
that depends... Continue Reading
To make objective
decisions about the processes that are critical to your
organization, you often need to examine categorical data. You may
know how to use a t-test or ANOVA when you’re comparing measurement
data (like weight, length, revenue, and so on), but do you know how to compare
attribute or counts data? It easy to do with statistical software
One person may look at
this bar... Continue Reading
you ever wonder why statistical analyses and concepts often have
such weird, cryptic names?
One conspiracy theory points to the workings of a secret
committee called the ICSSNN. The International Committee for
Sadistic Statistical Nomenclature and Numerophobia was formed
solely to befuddle and subjugate the masses. Its mission: To select
the most awkward, obscure, and confusing name possible... Continue Reading
2 of this blog series, I wrote about how statistical
inference uses data from a sample of individuals to reach
conclusions about the whole population. That’s a very powerful
tool, but you must check your assumptions when you make statistical
inferences. Violating any of these assumptions can result in false
positives or false negatives, thus invalidating your
The common... Continue Reading
In Part 1 of this
blog series, I wrote about how statistical inference uses data
from a sample of individuals to reach conclusions about the whole
population. That’s a very powerful tool, but you must check your
assumptions when you make statistical inferences. Violating any of
these assumptions can result in false positives or false negatives,
thus invalidating your results.
The common data... Continue Reading
If your work involves quality improvement, you've at least
heard of Design of Experiments (DOE). You probably know
it's the most efficient way to optimize and improve your process.
But many of us find DOE intimidating, especially if it's not a tool
we use often. How do you select an appropriate design, and ensure
you've got the right number of factors and levels? And after you've
gathered your... Continue Reading
With another Halloween almost upon us, here's a look back at
some of the posts we've written about this holiday specifically,
and about various creepy things in general. I hope that you enjoy
this roundup of 13 scary statistics posts...and that they won't
keep you up at night!
1. How to Make Minitab Wear a Halloween Costume
As Halloween nears, you can customize your Minitab interface to
match the... Continue Reading
Since the release of Minitab
Express in 2014, we’ve often received questions in technical
support about the differences between Express and Minitab 17.
In this post, I’ll attempt to provide a comparison between these
two Minitab products.
What Is Minitab 17?
Minitab 17 is an all-in-one graphical and statistical analysis
package that includes basic analysis tools such as hypothesis
testing,... Continue Reading
True or false: When comparing a parameter for two sets of
measurements, you should always use a hypothesis test to determine
whether the difference is statistically significant.
The answer? (drumroll...) True!
To understand this paradoxical answer, you need to keep in mind
the difference between samples, populations, and descriptive and
Descriptive Statistics and... Continue Reading
So the data you nurtured, that you worked so hard to format and
make useful, failed the normality test.
Time to face the truth: despite your best efforts, that data set
is never going to measure up to the assumption you may
have been trained to fervently look for.
Your data's lack of normality seems to make it poorly suited for
analysis. Now what?
Take it easy. Don't get uptight. Just let your data... Continue Reading
See if this
sounds fair to you. I flip a coin.
Heads: You win
$1.Tails: You pay me $1.
You may not like games of chance, but you have to admit it seems
like a fair game. At least, assuming the coin is a normal, balanced
coin, and assuming I’m not a sleight-of-hand magician who can
control the coin.
How about this next
You pay me $2 to play.I flip a coin over and over until
it comes up heads.Your... Continue Reading
Have you ever accidentally done statistics? Not all of us can
(or would want to) be “stat nerds,” but the word “statistics”
shouldn’t be scary. In fact, we all analyze things that happen to
us every day. Sometimes we don’t realize that we are compiling data
and analyzing it, but that’s exactly what we are doing. Yes, there
are advanced statistical concepts that can be difficult to
understand—but... Continue Reading
you perform a statistical analysis, you want to make sure you
collect enough data that your results are reliable. But you also
want to avoid wasting time and money collecting more data than you
need. So it's important to find an appropriate middle ground when
determining your sample size.
Now, technically, the Major League Baseball regular season isn't
a statistical analysis. But it does kind... Continue Reading
You often hear the data being
blamed when an analysis is not delivering the answers you wanted or
expected. I was recently reminded that the data chosen or collected
for a specific analysis is determined by the analyst, so there is
no such thing as bad data—only bad
This made me think about the
steps an analyst can take to minimise the risk of producing
analysis that fails to answer... Continue Reading
In statistics, t-tests are a type of hypothesis test that allows
you to compare means. They are called t-tests because each t-test
boils your sample data down to one number, the t-value. If you
understand how t-tests calculate t-values, you’re well on your way
to understanding how these tests work.
In this series of posts, I'm focusing on concepts rather than
equations to show how t-tests work.... Continue Reading
When it comes to statistical analyses, collecting a large enough
sample size is essential to obtaining quality results. If your
sample size is too small, confidence intervals may be too wide to
be useful, linear models may lack necessary precision, and
control charts may get so out of control that they become
self-aware and rise up against humankind.
Okay,that last point may have been... Continue Reading
Likert scales are commonly associated with surveys and are used in
a wide variety of settings. You’ve run into the Likert scale if
you’ve ever been asked whether you strongly agree, agree, neither
agree or disagree, disagree, or strongly disagree about something.
The worksheet to the right shows what five-point Likert data look
like when you have two groups.
Because Likert item data are... Continue Reading
P values have been around for nearly a century and they’ve been
the subject of criticism since their origins. In recent years, the
debate over P values has risen to a fever pitch. In particular,
there are serious fears that P values are misused to such an extent
that it has actually damaged science.
In March 2016, spurred on by the growing concerns, the American
Statistical Association (ASA) did... Continue Reading
I am a bit of an Oscar fanatic.
Every year after the ceremony, I religiously go online to find out
who won the awards and listen to their acceptance speeches. This
year, I was so chuffed to learn that Leonardo Di Caprio
won his first Oscar for his performance in The Revenant in
Awards—after five nominations in previous ceremonies. As a
longtime Di Caprio fan, I still remember... Continue Reading
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