Blog posts and articles about statistics principles and how they apply to quality improvement methods like Lean and Six Sigma.

"Data! Data! Data! I can't make bricks without clay."
— Sherlock Holmes, in Arthur Conan Doyle's The Adventure
of the Copper Beeches
Whether you're the world's greatest detective trying to crack a
case or a person trying to solve a problem at work, you're going to
need information. Facts. Data, as Sherlock Holmes
says.
But not all data is created equal, especially if you plan to
analyze as part of... Continue Reading

Choosing the right type of subgroup in a control chart is
crucial. In a rational subgroup, the variability within a subgroup
should encompass common causes, random, short-term variability and
represent “normal,” “typical,” natural process variations, whereas
differences between subgroups are useful to detect drifts in
variability over time (due to “special” or “assignable” causes).
Variation within... Continue Reading

You run a capability analysis
and your Cpk is bad. Now what?
First, let’s start by defining
what “bad” is. In simple terms, the smaller the Cpk, the more
defects you have. So the larger your Cpk is, the
better. Many
practitioners use a Cpk of 1.33 as the gold standard, so we’ll
treat that as the gold standard here, too.
Suppose we collect some data and run a capability analysis using
Minitab
Statisti... Continue Reading

In Part 1 of Gauging Gage, I looked at how adequate a
sampling of 10 parts is for a Gage R&R Study and providing
some advice based on the results.
Now I want to turn my attention to the other two factors in the
standard Gage experiment: 3 operators and 2 replicates.
Specifically, what if instead of increasing the number of parts in
the experiment (my previous post demonstrated you would need... Continue Reading

"You take 10 parts and have 3 operators measure each 2
times."
This standard approach to a Gage R&R experiment is so
common, so accepted, so ubiquitous that few people ever question
whether it is effective. Obviously one could look at whether
3 is an adequate number of operators or 2 an adequate number of
replicates, but in this first of a series of posts about
"Gauging Gage," I want to look at... Continue Reading

Everyone who analyzes data regularly has the experience of
getting a worksheet that just isn't ready to use. Previously I
wrote about tools you can use to
clean up and eliminate clutter in your data and
reorganize your data.
In this post, I'm going to
highlight tools that help you get the most out of messy data by
altering its characteristics.
Know Your Options
Many problems with data don't become... 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

In my last post, I wrote about
making a cluttered data set easier to work with by removing
unneeded columns entirely, and by displaying just those columns you
want to work with now. But
too much unneeded data isn't always the problem.
What can you do when someone
gives you data that isn't organized the way you need it to be?
That happens for a variety of
reasons, but most often it's because the... Continue Reading

In its industry guidance to companies that manufacture drugs and
biological products for people and animals,
the Food and Drug Administration (FDA) recommends three stages for
process validation:
Process Design,
Process Qualification, and Continued Process Verification. In
this post, we we will focus on that third stage.
Stage 3: Continued Process Verification
Per the FDA guidelines, the goal of... Continue Reading

People can make mistakes when they test a hypothesis with
statistical analysis. Specifically, they can make either Type I or
Type II errors.
As you analyze your own data and test hypotheses, understanding
the difference between Type I and Type II errors is extremely
important, because there's a risk of making each type of error in
every analysis, and the amount of risk is in your
control.
So
if... 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

Like
many, my introduction to 17th-century French philosophy came at the
tender age of 3+. For that is when I discovered the
Etch-a-Sketch®, an entertaining ode to Descartes' coordinate plane.
Little did I know that the seemingly idle hours I spent doodling
on my Etch-a-Sketch would prove to be excellent training for the
feat that I attempt today: plotting an Empirical Cumulative
Distribution... Continue Reading

My colleague Cody Steele wrote a post that
illustrated how
the same set of data can appear to support two contradictory
positions. He showed how changing the scale of a graph that
displays mean and median household income over time drastically
alters the way it can be interpreted, even though there's no change
in the data being presented.
When we analyze data, we need to present the results in... Continue Reading

Right
now I’m enjoying my daily dose of morning joe. As the steam rises
off the cup, the dark rich liquid triggers a powerful enzyme
cascade that jump-starts my brain and central nervous system,
delivering potent glints of perspicacity into the dark crevices of
my still-dormant consciousness.
Feels good, yeah! But is it good for me? Let’s see what the
studies say…
Drinking more than 4 cups of coffee... 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

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

by Matthew Barsalou, guest
blogger.
The old saying “if it walks like a duck, quacks like a duck and
looks like a duck, then it must be a duck” may be appropriate in
bird watching; however, the same idea can’t be applied when
observing a statistical distribution. The dedicated ornithologist
is often armed with binoculars and a field guide to the local birds
and this should be sufficient. A... Continue Reading

Have you ever wanted to know the odds of something happening, or
not happening?
It's the kind of question that students are frequently asked to
calculate by hand in introductory statistics classes, and going
through that exercise is a good way to become familiar with the
mathematical formulas the underlie probability (and hence, all of
statistics).
But let's be honest: when class is over, most... Continue Reading

In its industry guidance to companies that manufacture drugs and
biological products for people and animals, the Food and Drug
Administration (FDA) recommends three stages for process
validation. While
my last post covered
statistical tools for the Process Design stage, here we will
focus on the statistical techniques typically utilized for the
second stage, Process Qualification.
Stage 2: Process... Continue Reading

Have you ever wished your control charts were better? More
effective and user-friendly? Easier to understand and act
on? In this post, I'll share some simple ways to make SPC
monitoring more effective in Minitab.
Common Problems with SPC Control Charts
I
worked for several years in a large manufacturing plant in which
control charts played a very important role. Virtually thousands of
SPC... Continue Reading