Blog posts and articles about statistical power and sample size, especially in quality improvement projects.

Step 1 in our DOE problem-solving methodology
is to use process experts, literature, or past experiments to
characterize the process and define the problem. Since I had little
experience with golf myself, this was an important step for me.
This is not an uncommon situation. Experiment designers often
find themselves working on processes that they have little or no
experience with. For example, a... Continue Reading

Repeated measures designs don’t fit our impression of a typical
experiment in several key ways. When we think of an experiment, we
often think of a design that has a clear distinction between the
treatment and control groups. Each subject is in one, and only one,
of these non-overlapping groups. Subjects who are in a treatment
group are exposed to only one type of treatment. This is the... Continue Reading

Minitab 17 gives you the confidence you need to improve quality.

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By Matthew Barsalou, guest
blogger.
Many statistical tests assume the data being tested came from a
normal distribution. Violating the assumption of normality can
result in incorrect conclusions. For example, a Z test may indicate
a new process is more efficient than an older process when this is
not true. This could result in a capital investment for equipment
that actually results in higher... Continue Reading

In
my previous post, I wrote about the hypothesis testing ban in
the Journal of Basic and Applied Social Psychology. I
showed how P values and confidence intervals provide important
information that descriptive statistics alone don’t provide. In
this post, I'll cover the editors’ concerns about hypothesis
testing and how to avoid the problems they describe.
The editors describe hypothesis testing... Continue Reading

All processes have some
variation. Some variation is natural and nothing to be concerned
about. But in other cases, there is unusual variation that may need
attention.
By graphing process
data against an upper and a lower control limit, control charts
help us distinguish natural variation from special cause variation
that we need to be concerned about. If a data point falls outside
the limits on... 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

It’s safe to say that most people who use statistics are more
familiar with parametric analyses than nonparametric analyses.
Nonparametric tests are also called distribution-free tests because
they don’t assume that your data follow a specific
distribution.
You may have heard that you should use nonparametric tests when
your data don’t meet the assumptions of the parametric test,
especially the... Continue Reading

If you wanted to figure out the probability that your favorite
football team will win their next game, how would you do it?
My colleague
Eduardo Santiago and I recently looked at this question, and in
this post we'll share how we approached the solution. Let’s start
by breaking down this problem:
There are only two possible outcomes: your favorite team wins,
or they lose. Ties are a possibility,... Continue Reading

In my experience, one of the
hardest concepts for users to wrap their head around revolves
around the Power and Sample Size menu in Minitab's statistical software, and more specifically, the field that asks
for the "difference" or "difference to detect."
Let’s start with power. In statistics, the definition of power
is the probability that you will correctly reject the null
hypothesis when it is... Continue Reading

Stepwise regression and best subsets regression are both
automatic tools that help you identify useful predictors during the
exploratory stages of model building for linear regression. These
two procedures use different methods and present you with different
output.
An obvious question arises. Does one procedure pick the true
model more often than the other? I’ll tackle that question in this
post.
Fi... Continue Reading

Using a sample to estimate the properties of an entire population
is common practice in statistics. For example, the mean from a
random sample estimates that parameter for an entire population. In linear
regression analysis, we’re used to the idea that the regression coefficients are estimates of the
true parameters. However, it’s easy to forget that R-squared
(R2) is also an estimate.... Continue Reading

Do you suffer from PAAA (Post-Analysis Assumption Angst)? You’re
not alone.
Checking the required assumptions for a statistical
analysis is critical. But if you don’t have a Ph.D. in statistics,
it can feel more complicated and confusing than the primary
analysis itself.
How
does the
cuckoo egg data, a common sample data set often used to teach
analysis of variance, satisfy the following
formal... Continue Reading

A few weeks
ago I looked at the
number of goals that were being scored in the World Cup. At the
time there were 2.9 goals per game, which was the highest since
1970. Unfortunately for spectators who enjoyed the higher scoring
goals, this did not last.
By the end, the average had fallen to 2.7 goals per game, the
same amount scored in the 1998 World Cup. After such a high-scoring
start, the goals... Continue Reading

Remember
"The Little Engine That Could," the children's story about
self-confidence in the face of huge challenges? In it, a train
engine keeps telling itself "I think I can" while carrying a very
heavy load up a big mountain. Next thing you know, the little
engine has done it...but until that moment, the outcome
was uncertain.
It's a wonderful story for teaching kids about self-confidence.
But... Continue Reading

Minitab graphs are powerful tools for investigating your process
further and removing any doubt about the steps you should take to
improve it. With that in mind, you’ll want to know every feature
about Minitab graphs that can help you share and communicate your
results effectively. While many ways to modify your graph are on
the Editor menu, some of the best features become
available when you... Continue Reading

It's all too easy to make mistakes involving statistics.
Powerful statistical software can remove a lot of the difficulty
surrounding statistical calculation, reducing the risk of
mathematical errors—but correctly interpreting the results of
an analysis can be even more challenging.
No one knows that better than Minitab's technical trainers. All of our trainers
are seasoned statisticians with... Continue Reading

We’re in the
thick of the Stanley Cup playoffs, which means hockey fans are
doing what seems to be every sports fan's favorite
hobby...complaining about the refs! While most complaints, such as
“We’re not getting any of the close calls!” are subjective and hard
to get data for, there's one question that we should be able to
answer objectively with a statistical analysis: Are hockey
penalties... Continue Reading

One-way
ANOVA can detect differences between the means of three or more
groups. It’s such a classic statistical analysis that it’s hard to
imagine it changing much.
However, a revolution has been under way for a while now.
Fisher's classic one-way ANOVA, which is taught in Stats 101
courses everywhere, may well be obsolete thanks to Welch’s
ANOVA.
In this post, I not only want to introduce you to... Continue Reading

My
previous post examined how an equivalence test
can shift the burden of proof when you perform hypothesis test of
the means. This allows you to more rigorously test whether the
process mean is equivalent to a target or to another mean.
Here’s another key difference: To perform the analysis, an
equivalence test requires that you first define, upfront, the size
of a practically important difference... Continue Reading

I’ve
written a number of blog posts about regression analysis and I've
collected them here to create a regression tutorial. I’ll
supplement my own posts with some from my colleagues.
This tutorial covers many aspects of regression analysis
including: choosing the type of regression analysis to use,
specifying the model, interpreting the results, determining how
well the model fits, making... Continue Reading