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

Previously, I showed why there is no R-squared for nonlinear regression. Anyone
who uses nonlinear regression will also notice that there are no P
values for the predictor variables. What’s going on?
Just like there are good reasons not to calculate R-squared for
nonlinear regression, there are also good reasons not to calculate
P values for the coefficients.
Why not—and what to use instead—are the... Continue Reading

by The Discrete Sharer, guest blogger
As Minitab users, many of us have found
staged control charts to be an effective tool to
quantify and demonstrate the “before and after” state of our
process improvement activities.
However, have you ever considered using them to demonstrate the
effects of changes to compensation/incentive plans for your
employees?
Here's an example of how a mid-sized... Continue Reading

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Screening experimental designs allow you to study a very large
number of factors in a very limited number of runs. The objective
is to focus on the few factors that have a real effect and
eliminate the effects that are not significant. This is often the
initial typical objective of any experimenter when a
DOE (design of experiments) is performed.
Table of Factorial Designs
Consider the table below.... Continue Reading

Control charts are some of the most useful tools in statistical
science. They track process statistics over time and detect when
the mean or standard deviation change from what they have been. The
signals that control charts send about special causes can help you
zero in on the fastest ways to improve any process, whether you’re
making tires,
turbines, or
trying to improve patient care.
I’ve
menti... Continue Reading

After upgrading to the latest
and greatest version of our statistical software, Minitab 17, some
users have contacted tech support to ask "Wait a minute, where is
that Two-Way ANOVA option in Minitab 17?"
The answer is that it’s not
there. That’s right! The 2-Way ANOVA option that was available in
Minitab 16 and prior versions was removed from Minitab 17.Why would this feature be removed from the... Continue Reading

In
Part I and
Part II we learned about the experiment and the survey,
respectively. Now we turn our attention to the results...
Our
first two participants, Danielle and Sheryl, enter the conference
room and are given blindfolds as we explain how the experiment will
proceed. As we administer the tasting, the colors of the wine
are obvious but we don't know the true types, which have been
masked... Continue Reading

In
Blind Wine Part I, we introduced our experimental setup, which
included some survey questions asked ahead of time of each
participant. The four questions asked were:
On a scale of 1 to 10, how would you rate your knowledge of
wine?
How much would you typically spend on a bottle of wine in a
store?
How many different types of wine (merlot, riesling, cabernet,
etc.) would you buy regularly (not as... Continue Reading

A
recent study has indicated that female-named hurricanes kill more people than male
hurricanes. Of course, the title of that article (and other
articles like it) is a bit misleading. The study found a
significant
interaction between the damage caused by the storm and the
perceived masculinity or femininity of the hurricane names. So
don’t be confused by stories that suggest all... 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

In Minitab, the Assistant menu is your interactive guide to choosing
the right tool, analyzing data correctly, and interpreting the
results. If you’re feeling a bit rusty with choosing and using a
particular analysis, the Assistant is your friend!
Previously, I’ve written about the new linear model features in Minitab 17. In
this post, I’ll work through a multiple regression analysis example
and... Continue Reading

We received the following question via social media
recently:
I am using Minitab 17 for ANOVA.
I calculated the mean and standard deviation for these 15 values,
but the standard deviation is very high. If I delete some values, I
can reduce the standard deviation. Is there an option in Minitab
that will automatically indicate values that are out of range and
delete them so that the standard... Continue Reading

There is more than just the p value in a probability plot—the
overall graphical pattern also provides a great deal of useful
information. Probability plots are a powerful tool to better
understand your data.
In this post, I intend to present the main principles of
probability plots and focus on their visual interpretation using
some real data.
In probability plots, the data density distribution... 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

There is high pressure to find low P values. Obtaining a low P
value for a hypothesis test is make or break because it can lead to
funding, articles, and prestige. Statistical significance is
everything!
My two previous posts looked at several issues related to P
values:
P values have a higher than expected false positive
rate.
The same P value from different studies can
correspond to different false... Continue Reading

by Matthew Barsalou, guest blogger
Programs such as the Minitab Statistical
Software make hypothesis testing easier; but no program can
think for the experimenter. Anybody performing a statistical
hypothesis test must understand what p values mean in regards to
their statistical results as well as potential limitations of
statistical hypothesis testing.
A p value of 0.05 is frequently used during... Continue Reading

The
interpretation of P values would seem to be fairly standard between
different studies. Even if two hypothesis tests study different
subject matter, we tend to assume that you can interpret a P value
of 0.03 the same way for both tests. A P value is a P value,
right?
Not so fast! While Minitab statistical software can correctly calculate all P
values, it can’t factor in the larger context of the... Continue Reading

The P
value is used all over statistics, from t-tests to regression analysis. Everyone knows that you
use P values to determine statistical significance in a hypothesis
test. In fact, P values often determine what studies get published
and what projects get funding.
Despite being so important, the P value is a slippery concept
that people often interpret incorrectly. How do you
interpret P values?
In... 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

With
more options, come more decisions.
With equivalence testing added to Minitab 17, you now have more
statistical tools to test a sample mean against target value or
another sample mean.
Equivalence testing is extensively used in the biomedical field.
Pharmaceutical manufacturers often need to test whether the
biological activity of a generic drug is equivalent to that of a
brand name drug that... Continue Reading