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
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
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
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
How deeply has statistical content from Minitab blog posts (or
other sources) seeped into your brain tissue? Rather than submit a
biopsy specimen from your temporal lobe for analysis, take this
short quiz to find out. Each question may have more than one
correct answer. Good luck!
of the following are famous figure skating pairs, and which are
methods for testing whether your data follow a... Continue Reading
Back when I was an undergrad in
statistics, I unfortunately spent an entire semester of my life
taking a class, diligently crunching numbers with my TI-82, before
realizing 1) that I was actually in an Analysis of Variance (ANOVA)
class, 2) why I would want to use such a tool in the first place,
and 3) that ANOVA doesn’t necessarily tell you a thing about
Fortunately, I've had a lot more... Continue Reading
Control charts are a fantastic tool. These charts plot your
process data to identify common cause and special cause variation.
By identifying the different causes of variation, you can take
action on your process without over-controlling it.
Assessing the stability of a process can help you determine
whether there is a problem and identify the source of the problem.
Is the mean too high, too low,... Continue Reading
By Matthew Barsalou, guest
A problem must be understood before it can be properly
addressed. A thorough understanding of the problem is critical when
root cause analysis (RCA) and an RCA is necessary if an
organization wants to implement corrective actions that truly
address the root cause of the problem. An RCA may also be necessary
for process improvement projects; it is... Continue Reading
You've collected a bunch of
data. It wasn't easy, but you did it. Yep, there it is, right
there...just look at all those numbers, right there in neat columns
and rows. Congratulations.
I hate to ask...but what are you
going to do with your data?
If you're not sure precisely
what to do with the data you've got, graphing it is a
great way to get some valuable insight and direction. And a good
graph to... 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
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
removed the errant post. But to my
surprise, the reader who helped me... Continue Reading
As a Minitab
trainer, one of the most common questions I get from training
participants is "what should I do when my data isn’t normal?" A
large number of statistical tests are based on the assumption of
normality, so not having data that is normally distributed
typically instills a lot of fear.
Many practitioners suggest that if your data are not normal, you
should do a nonparametric version of... Continue Reading
In this series of posts, I show how hypothesis tests and
confidence intervals work by focusing on concepts and graphs rather
than equations and numbers.
Previously, I used graphs to show what statistical significance really
means. In this post, I’ll explain both confidence intervals and
confidence levels, and how they’re closely related to P values and
How to Correctly... Continue Reading
This is a companion post for a series of blog posts about
understanding hypothesis tests. In this series, I create a
graphical equivalent to a 1-sample t-test and confidence interval
to help you understand how it works more intuitively.
This post focuses entirely on the steps required to create the
graphs. It’s a fairly technical and task-oriented post designed for
those who need to create the... Continue Reading
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