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
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
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
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
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
The language of statistics is a funny thing, but there usually
isn't much to laugh at in the consequences that can follow when
misunderstandings occur between statisticians and
non-statisticians. We see these consequences frequently in the
media, when new studies—that usually contradict previous ones—are
breathlessly related, as if their findings were incontrovertible
Similar, though less... Continue Reading
2016 comes to a close, it’s time to reflect on the passage of time
and changes. As I’m sure you’ve guessed, I love statistics and
analyzing data! I also love talking and writing about it. In fact,
I’ve been writing statistical blog posts for over five years, and
it’s been an absolute blast. John Tukey, the renowned statistician,
once said, “The best thing about being a statistician... Continue Reading
week we’re celebrating the annual Thanksgiving holiday in the
United States, which is not only a good time to reflect on the
things we’re grateful for, but it’s also a good time to stuff
yourself with turkey, mashed potatoes, green bean casserole, and
the usual suspects that find their way to the Thanksgiving
While I’m of course very thankful for my family, friends, home,
etc., I’m also... Continue Reading
In Part 1 of this
blog series, I wrote about how statistical inference uses data
from a sample of individuals to reach conclusions about the whole
population. That’s a very powerful tool, but you must check your
assumptions when you make statistical inferences. Violating any of
these assumptions can result in false positives or false negatives,
thus invalidating your results.
The common data... Continue Reading
If you’re not a statistician, looking through statistical output
can sometimes make you feel a bit like Alice in
Wonderland. Suddenly, you step into a fantastical world
where strange and mysterious phantasms appear out of nowhere.
For example, consider the T and P in your t-test results.
“Curiouser and curiouser!” you might exclaim, like Alice, as you
gaze at your output.
What are these values,... Continue Reading
Data mining can be helpful in the exploratory phase of an
analysis. If you're in the early stages and you're just figuring
out which predictors are potentially correlated with your response
variable, data mining can help you identify candidates. However,
there are problems associated with using data mining to select
In my previous post, we used data mining to settle on
the following... Continue Reading
Since the release of Minitab
Express in 2014, we’ve often received questions in technical
support about the differences between Express and Minitab 17.
In this post, I’ll attempt to provide a comparison between these
two Minitab products.
What Is Minitab 17?
Minitab 17 is an all-in-one graphical and statistical analysis
package that includes basic analysis tools such as hypothesis
testing,... Continue Reading
We hosted our first-ever Minitab Insights conference in
September, and if you were among the attendees, you already know
the caliber of the speakers and the value of the information they
shared. Experts from a wide range of industries offered a lot of
great lessons about how they use data analysis to improve business
practices and solve a variety of problems.
I blogged earlier about five key...Continue Reading
watched an old motorcycle flick from the 1960s the other night, and I
was struck by the bikers' slang. They had a language all their own.
Just like statisticians, whose manner of speaking often confounds
those who aren't hep to the lingo of data analysis.
It got me thinking...what if there were an all-statistician
biker gang? Call them the Nulls Angels. Imagine them in their
colors, tearing... Continue Reading
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
If you’re in the market for statistical software, there are many
considerations and more than a few options for you to evaluate.
Check out these seven questions to ask yourself before choosing
statistical software—your answers should help guide you towards the
best solution for your needs!
1. Who uses statistical software in your organization?
Are they expert statisticians, novices, or a mix of both?... Continue Reading
In 2011 we had solar panels fitted on our property. In the last
few months we have noticed a few problems with the inverter (the
equipment that converts the electricity generated by the panels
from DC to AC, and manages the transfer of unused electric to the
power company). It was shutting down at various times throughout
the day, typically when it was very sunny, resulting in no
electricity being... 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
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