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
Statistics can be challenging, especially if you're not
analyzing data and interpreting the results every day. Statistical
software makes things easier by handling the arduous
mathematical work involved in statistics. But ultimately, we're
responsible for correctly interpreting and communicating what the
results of our analyses show.
The p-value is probably the most frequently cited
statistic. We... 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
Histograms are one of the
most common graphs used to display numeric data. Anyone who
takes a statistics course is likely to learn about the histogram,
and for good reason: histograms are easy to understand and can
instantly tell you a lot about your data.
Here are three of the most important things you can learn by
looking at a histogram.
Shape—Mirror, Mirror, On the Wall…
If the left side of a... Continue Reading
2016 comes to a close, it’s time to reflect on the passage of time
and changes. As I’m sure you’ve guessed, I love statistics and
analyzing data! I also love talking and writing about it. In fact,
I’ve been writing statistical blog posts for over five years, and
it’s been an absolute blast. John Tukey, the renowned statistician,
once said, “The best thing about being a statistician... 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 you’re not a statistician, looking through statistical output
can sometimes make you feel a bit like Alice in
Wonderland. Suddenly, you step into a fantastical world
where strange and mysterious phantasms appear out of nowhere.
For example, consider the T and P in your t-test results.
“Curiouser and curiouser!” you might exclaim, like Alice, as you
gaze at your output.
What are these values,... Continue Reading
Statistical inference uses data from a sample of individuals to
reach conclusions about the whole population. It’s a very
powerful tool. But as the saying goes, “With great
power comes great responsibility!” When attempting to make
inferences from sample data, you must check your assumptions.
Violating any of these assumptions can result in false positives or
false negatives, thus invalidating... Continue Reading
watched an old motorcycle flick from the 1960s the other night, and I
was struck by the bikers' slang. They had a language all their own.
Just like statisticians, whose manner of speaking often confounds
those who aren't hep to the lingo of data analysis.
It got me thinking...what if there were an all-statistician
biker gang? Call them the Nulls Angels. Imagine them in their
colors, tearing... 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
Earlier this month, PLOS.org
published an article titled "Ten Simple Rules for Effective Statistical
10 rules are good reading for anyone who draws conclusions and makes decisions
based on data, whether
you're trying to extend the boundaries of scientific knowledge or
make good decisions for your business.
Carnegie Mellon University's
Robert E. Kass and several co-authors devised... Continue Reading
What does the eyesight of a homeless person have in common with
complications from dental anesthesia? Or with reducing
side-effects from cancer? Or monitoring artificial hip
These are all subjects of recently published studies that
analyses in Minitab to improve healthcare
outcomes. And they're a good reminder that when we
improve the quality of healthcare for others, we... Continue Reading
Analysis of variance (ANOVA) can determine whether the means of
three or more groups are different. ANOVA uses F-tests to
statistically test the equality of means. In this post, I’ll show
you how ANOVA and F-tests work using a one-way ANOVA example.
But wait a minute...have you ever stopped to wonder why you’d
use an analysis of variance to determine whether
means are different? I'll also show how... 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
first part of this series, we looked at a case study where
staff at a hospital used ATP swab tests to test 8 surfaces for
bacteria in 10 different hospital rooms across 5 departments. ATP
measurements below 400 units pass the swab test, while measurements
greater than or equal to 400 units fail the swab test and require
offered two tips on exploring and visualizing... Continue Reading
T-tests are handy hypothesis tests in statistics when you want to
compare means. You can compare a sample mean to a hypothesized or
target value using a one-sample t-test. You can compare the means
of two groups with a two-sample t-test. If you have two groups with
paired observations (e.g., before and after measurements), use the
How do t-tests work? How do t-values fit in? In this... 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
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