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Power Analysis

Blog posts and articles about assessing the power of a statistical analysis.

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

7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

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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
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 like Minitab.  One person may look at this bar... Continue Reading
Did you ever wonder why statistical analyses and concepts often have such weird, cryptic names? One conspiracy theory points to the workings of a secret committee called the ICSSNN. The International Committee for Sadistic Statistical Nomenclature and Numerophobia was formed solely to befuddle and subjugate the masses. Its mission: To select the most awkward, obscure, and confusing name possible... 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 facts. Similar, though less... Continue Reading
In Parts 1 and 2 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... Continue Reading
Dear Readers, As 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
The season of change is upon us here at Minitab's World Headquarters. The air is crisp and clear and the landscape is ablaze in vibrant fall colors. As I drove to work one recent morning, I couldn't help but soak in the beauty surrounding me and think, "Too bad everything they taught me as a kid was a lie." You see, as a boy growing up in New Hampshire, I was told that the sublime beauty of autumn... Continue Reading
Pareto charts are a special type of bar chart you can use to prioritize almost anything. This makes them very useful in making sound decisions. For example, if you have several possible quality improvement projects, but not enough time or people to do them all now, you can use a Pareto chart to identify which projects have the most potential for making meaningful improvement. Pareto charts look... 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 your work involves quality improvement, you've at least heard of Design of Experiments (DOE). You probably know it's the most efficient way to optimize and improve your process. But many of us find DOE intimidating, especially if it's not a tool we use often. How do you select an appropriate design, and ensure you've got the right number of factors and levels? And after you've gathered your... Continue Reading
With another Halloween almost upon us, here's a look back at some of the posts we've written about this holiday specifically, and about various creepy things in general. I hope that you enjoy this roundup of 13 scary statistics posts...and that they won't keep you up at night! 1. How to Make Minitab Wear a Halloween Costume As Halloween nears, you can customize your Minitab interface to match the... Continue Reading
Statistical inference uses data from a sample of individuals to reach conclusions about the whole population. It’s a very powerful tool. But as the saying goes, “With great power comes great responsibility!” When attempting to make inferences from sample data, you must check your assumptions. Violating any of these assumptions can result in false positives or false negatives, thus invalidating... 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
If you were among the 300 people who attended the first-ever Minitab Insights conference in September, you already know how powerful it was. Attendees learned how practitioners from a wide range of industries use data analysis to address a variety of problems, find solutions, and improve business practices. In the coming weeks and months, we will share more of the great insights and guidance shared... Continue Reading
There may be huge potential benefits waiting in the data in your servers. These data may be used for many different purposes. Better data allows better decisions, of course. Banks, insurance firms, and telecom companies already own a large amount of data about their customers. These resources are useful for building a more personal relationship with each customer. Some organizations already use... 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
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