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Statistics Help

Blog posts and articles that offer tips about the statistics used in lean and six sigma quality improvement projects.

An exciting new study sheds light on the relationship between P values and the replication of experimental results. This study highlights issues that I've emphasized repeatedly—it is crucial to interpret P values correctly, and significant results must be replicated to be trustworthy. The study also supports my disagreement with the decision by the Journal of Basic and Applied Social Psychology to b... 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

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

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When I started out on the blog, I spent some time showing some data sets that would be easy to illustrate statistical concepts. It’s easier to show someone how something works with something familiar than with something they’ve never thought about before. Need a quick illustration to share with someone about how to summarize a variable in Minitab? See if they have a magazine on their desk, and... Continue Reading
Whatever industry you're in, you're going to need to buy supplies. If you're a printer, you'll need to purchase inks, various types of printing equipment, and paper. If you're in manufacturing, you'll need to obtain parts that you don't make yourself.  But how do you know you're making the right choice when you have multiple suppliers vying to fulfill your orders?  How can you be sure you're... 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
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
Last time, I told you how I had double-checked the analysis in a post that involved running the Johnson transformation on a set of data before doing normal capability analysis on it. A reader asked why the transformation didn't work on the data when you applied it outside of the capability analysis.  I hadn't tried transforming the data that way, but if the transformation worked when performed as... Continue Reading
Every now and then I’ll test my Internet speed at home using such sites as http://speedtest.comcast.net.  My need to perform these tests could stem from the cool-looking interfaces they employ on their site, as they display the results using analog speedometers and RPM meters. They could also stem from the validation that I need in "getting what I am paying for," although I realize that there are... Continue Reading
Last month the ESPN series Outside the Lines reported on major league pitchers suffering serious injuries from being struck in the head by line drives, and efforts MLB is making towards having protective gear developed for pitchers. You can view the report here if you'd like: A couple of things jump out at me from the clip: The overwhelming majority of pitchers are not interested in wearing... 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
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
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 1898, Russian economist Ladislaus Bortkiewicz published his first statistics book entitled Das Gesetz der keinem Zahlen, in which he included an example that eventually became famous for illustrating the Poisson distribution. Bortkiewicz researched the annual deaths by horse kicks in the Prussian Army from 1875-1984. Data was recorded from 14 different army corps, with one being the Guard... 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 significance levels. How to Correctly... Continue Reading
Minitab 17.2 is available. You can check out all of the new stuff on the What’s New Page, but I would say that a little demonstration is in order. Here are some new shortcuts that make arranging your data easier in Minitab 17.2. Sorting Let’s suppose that you had copied some Human Development Index data into Minitab and wanted to sort countries in the order of their rank. In the past, you would have... Continue Reading
To choose the right statistical analysis, you need to know the distribution of your data. Suppose you want to assess the capability of your process. If you conduct an analysis that assumes the data follow a normal distribution when, in fact, the data are nonnormal, your results will be inaccurate. To avoid this costly error, you must determine the distribution of your data. So, how do you determine... Continue Reading
Imagine that you are watching a race and that you are located close to the finish line. When the first and fastest runners complete the race, the differences in times between them will probably be quite small. Now wait until the last runners arrive and consider their finishing times. For these slowest runners, the differences in completion times will be extremely large. This is due to the fact that... Continue Reading