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Normal Distribution

Blog posts and articles about the role of the normal distribution in statistics, data analysis, and quality improvement.

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
See if this sounds fair to you. I flip a coin. Heads: You win $1.Tails: You pay me $1. You may not like games of chance, but you have to admit it seems like a fair game. At least, assuming the coin is a normal, balanced coin, and assuming I’m not a sleight-of-hand magician who can control the coin. How about this next game? You pay me $2 to play.I flip a coin over and over until it comes up heads.Your... Continue Reading

7 Deadly Statistical Sins Even the Experts Make

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Back when I used to work in Minitab Tech Support, customers often asked me, “What’s the difference between Cpk and Ppk?” It’s a good question, especially since many practitioners default to using Cpk while overlooking Ppk altogether. It’s like the '80s pop duo Wham!, where Cpk is George Michael and Ppk is that other guy. Poofy hairdos styled with mousse, shoulder pads, and leg warmers aside, let’s... Continue Reading
Here is a scenario involving process capability that we’ve seen from time to time in Minitab's technical support department. I’m sharing the details in this post so that you’ll know where to look if you encounter a similar situation. You need to run a capability analysis. You generate the output using Minitab Statistical Software. When you look at the results, the Cpk is huge and the histogram in... Continue Reading
In my last post, we took the red pill and dove deep into the unarguably fascinating and uncompromisingly compelling world of the matrix plot. I've stuffed this post with information about a topic of marginal interest...the marginal plot. Margins are important. Back in my English composition days, I recall that margins were particularly prized for the inverse linear relationship they maintained with... Continue Reading
Earlier this month, PLOS.org published an article titled "Ten Simple Rules for Effective Statistical Practice." The 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
For one reason or another, the response variable in a regression analysis might not satisfy one or more of the assumptions of ordinary least squares regression. The residuals might follow a skewed distribution or the residuals might curve as the predictions increase. A common solution when problems arise with the assumptions of ordinary least squares regression is to transform the response... Continue Reading
For hundreds of years, people having been improving their situation by pulling themselves up by their bootstraps. Well, now you can improve your statistical knowledge by pulling yourself up by your bootstraps. Minitab Express has 7 different bootstrapping analyses that can help you better understand the sampling distribution of your data.  A sampling distribution describes the likelihood of... Continue Reading
Once upon a time, when people wanted to compare the standard deviations of two samples, they had two handy tests available, the F-test and Levene's test. Statistical lore has it that the F-test is so named because it so frequently fails you.1 Although the F-test is suitable for data that are normally distributed, its sensitivity to departures from normality limits when and where it can be used. Leve... Continue Reading
In the first part of this series, we looked at a case study where staff at a hospital used ATP swab tests to test 8 surfaces for bacteria in 10 different hospital rooms across 5 departments. ATP measurements below 400 units pass the swab test, while measurements greater than or equal to 400 units fail the swab test and require further investigation. I offered two tips on exploring and visualizing... Continue Reading
Working with healthcare-related data often feels different than working with manufacturing data. After all, the common thread among healthcare quality improvement professionals is the motivation to preserve and improve the lives of patients. Whether collecting data on the number of patient falls, patient length-of-stay, bed unavailability, wait times, hospital acquired-infections, or readmissions,... 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 paired t-test. How do t-tests work? How do t-values fit in? In this... Continue Reading
About a year ago, a reader asked if I could try to explain degrees of freedom in statistics. Since then,  I’ve been circling around that request very cautiously, like it’s some kind of wild beast that I’m not sure I can safely wrestle to the ground. Degrees of freedom aren’t easy to explain. They come up in many different contexts in statistics—some advanced and complicated. In mathematics, they're... Continue Reading
Five-point Likert scales are commonly associated with surveys and are used in a wide variety of settings. You’ve run into the Likert scale if you’ve ever been asked whether you strongly agree, agree, neither agree or disagree, disagree, or strongly disagree about something. The worksheet to the right shows what five-point Likert data look like when you have two groups. Because Likert item data are... Continue Reading
In my last post, I discussed how a DOE was chosen to optimize a chemical-mechanical polishing process in the microelectronics industry. This important process improved the plant's final manufacturing yields. We selected an experimental design that let us study the effects of six process parameters in 16 runs. Analyzing the Design Now we'll examine the analysis of the DOE results after the actual... Continue Reading
Like so many of us, I try to stay healthy by watching my weight. I thought it might be interesting to apply some statistical thinking to the idea of maintaining a healthy weight, and the central limit theorem could provide some particularly useful insights. I’ll start by making some simple (maybe even simplistic) assumptions about calorie intake and expenditure, and see where those lead. And then... Continue Reading
There's nothing like a boxplot, aka box-and-whisker diagram, to get a quick snapshot of the distribution of your data. With a single glance, you can readily intuit its general shape, central tendency, and variability. To easily compare the distribution of data between groups, display boxplots for the groups side by side. Visually compare the central value and spread of the distribution for each... 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! Which of the following are famous figure skating pairs, and which are methods for testing whether your data follow a... Continue Reading
When you work in data analysis, you quickly discover an irrefutable fact: a lot of people just can't stand statistics. Some people fear the math, some fear what the data might reveal, some people find it deadly dull, and others think it's bunk. Many don't even really know why they hate statistics—they just do. Always have, probably always will.  Problem is, that means we who analyze data need to com... Continue Reading
There are many reasons why a distribution might not be normal/Gaussian. A non-normal pattern might be caused by several distributions being mixed together, or by a drift in time, or by one or several outliers, or by an asymmetrical behavior, some out-of-control points, etc. I recently collected the scores of three different teams (the Blue team, the Yellow team and the Pink team) after a laser... Continue Reading