Blog posts and articles about testing hypotheses with the statistical method called the T-Test.

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

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If
you use ordinary linear regression with a response of count data,
if may work out fine (Part
1), or you may run into some problems (Part
2).
Given that a count response could be problematic, why not use a
regression procedure developed to handle a response of counts?
A Poisson regression analysis is designed to analyze a
regression model with a count response.
First, let's try using Poisson... Continue Reading

Ever use dental floss to cut soft cheese? Or Alka Seltzer to
clean your toilet bowl? You can find a host of nonconventional uses for ordinary objects
online. Some are more peculiar than others.
Ever use ordinary linear regression to evaluate a response
(outcome) variable of counts?
Technically, ordinary linear regression was designed to evaluate
a a continuous response variable. A continuous... Continue Reading

In regression
analysis, overfitting a model is a real problem. An overfit model
can cause the regression coefficients, p-values, and R-squared to be misleading. In this post,
I explain what an overfit model is and how to detect and avoid this
problem.
An overfit model is one that is too complicated for your data
set. When this happens, the regression model becomes tailored to
fit the quirks and... Continue Reading

Rare events inherently occur in all kinds of
processes. In hospitals, there are medication errors, infections,
patient falls, ventilator-associated pneumonias, and other rare,
adverse events that cause prolonged hospital stays and increase
healthcare costs.
But rare events happen in many
other contexts, too. Software developers may need to track errors
in lines of programming code, or a quality... 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
like Minitab.
One person may look at
this bar... Continue Reading

When we take pictures with a
digital camera or smartphone, what the device really does
is capture information in the form of binary code. At the most
basic level, our precious photos are really just a bunch of 1s and
0s, but if we were to look at them that way, they'd be pretty
unexciting.
In its raw state, all that
information the camera records is worthless. The 1s and 0s need to be converted... Continue Reading

When performing a design of experiments (DOE), some factor
levels may be very difficult to change—for example, temperature
changes for a furnace. Under these circumstances, completely
randomizing the order in which tests are run becomes almost
impossible.To minimize the number of factor level changes for a
Hard-to-Change (HTC) factor, a
split-plot design is required.
Why Do We Want to Randomize a... Continue Reading

Statisticians say the darndest things. At least, that's how it
can seem if you're not well-versed in statistics.
When I began studying statistics, I approached it as a language.
I quickly noticed that compared to other disciplines, statistics
has some unique problems with terminology, problems that don't
affect most scientific and academic specialties.
For
example, dairy science has a highly... Continue Reading

If you've read the first two
parts of this tale, you know
it started when I published a post that involved transforming
data for capability analysis. When an astute reader asked why
Minitab didn't seem to transform the data outside of the capability
analysis, it revealed
an oversight that invalidated the original
analysis.
I
removed the errant post. But to my
surprise, the reader who helped me... Continue Reading

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

Before I joined Minitab, I worked for many years in Penn State's
College of Agricultural Sciences as a writer and editor. I
frequently wrote about food science and particularly food safety,
as I regularly needed to report on the research being conducted by
Penn State's food safety experts, and also edited course materials
and bulletins for professionals and consumers about ensuring they
had safe... Continue Reading

Previously, I’ve written about
how to interpret regression coefficients and their individual P
values.
I’ve also written about
how to interpret R-squared to assess the strength of the
relationship between your model and the response variable.
Recently I've been asked, how does the F-test of the overall
significance and its P value fit in with these other statistics?
That’s the topic of this post!
In... Continue Reading

I recently fielded an interesting question about the probability
and survival plots in Minitab Statistical
Software's Reliability/Survival menus:
Is there a one-to-one match
between the confidence interval points on a probability plot and
the confidence interval points on survival plot at a specific
percentile?
Now, this may seem like an easy question, given that the
probabilities on a survival plot... Continue Reading

Scientists who use the Hubble Space Telescope to explore the
galaxy receive a stream of digitized images in the form binary
code. In this state, the information is essentially worthless-
these 1s and 0s must first be converted into pictures before the
scientists can learn anything from them.
The same is true of statistical distributions and parameters that are used to describe sample data. They... Continue Reading

The NFL recently announced that after scoring a touchdown, teams
will be required to kick the extra point from the 15 yard line as
opposed to the 2 yard line. This is a pretty big change. And
whether you’re trying to improve the quality of your process, or
simply trying to make a sporting event more exciting, it’s always
good to know what kind of effects your change will have. So I’m
going to use... Continue Reading

Earlier, I wrote about the
different types of data statisticians typically encounter. In
this post, we're going to look at why, when given a choice in the
matter, we prefer to analyze continuous data rather than
categorical/attribute or discrete data.
As a reminder, when we assign something to a group or give it a
name, we have created attribute or
categorical data. If we count something,
like... 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

Banned! In February 2015, editor David Trafimow and associate
editor Michael Marks of the Journal of Basic and Applied Social
Psychology declared that the null hypothesis statistical
testing procedure is invalid. They promptly banned P values,
confidence intervals, and hypothesis testing from the journal.
The journal now requires descriptive statistics and effect
sizes. They also encourage large... Continue Reading