---
title: "The Minitab Blog Quiz: Test Your Stat-Smarts!"
description: "The Minitab Blog Quiz: Test Your Stat-Smarts!"
---

[Minitab Blog](https://blog.minitab.com/en/blog)

# [The Minitab Blog Quiz: Test Your Stat-Smarts!](https://blog.minitab.com/en/blog/statistics-and-quality-data-analysis/the-minitab-blog-quiz-test-your-stat-smarts)

 Written by [Minitab Blog Editor](https://blog.minitab.com/en/blog/author/minitab-blog-editor) | Jan 15, 2016 5:00:00 AM

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!

1. **Which of the following are famous figure skating pairs, and which are methods for testing whether your data follow a normal distribution?**
   
    a. Belousova-Protopopov  
    b. Anderson-Darling  
    c. Kolmogorov-Smirnov  
    d. Shen-Zhao  
    e. Shapiro-Wilk  
    f. Salé-Pelletier  
    g. Ryan-Joiner
   
     
   
   | Figure skaters are **a**, **d**, and **f**. Methods for testing normality are **b**, **c**, **e**, and **g**. To learn about the different methods for testing normality in Minitab, click [here](https://blog.minitab.com/blog/the-statistical-mentor/anderson-darling-ryan-joiner-or-kolmogorov-smirnov-which-normality-test-is-the-best?hsLang=en). |
   | --- |
   
     
2. **A t-value is so-named because...**
   
    a. Its value lies midway between the standard deviation(s) and the u-value coefficient (u).  
    b. It was first calculated in Fisher’s famous “Lady Tasting Tea” experiment.  
    c. It comes from a t-distribution.  
    d. It’s the first letter of the last name of the statistician who first defined it.  
    e. It was originally estimated by reading tea leaves.
   
     
   
   | The correct answer is **c**. To find out what the t-value means, read [this post](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/what-are-t-values-and-p-values-in-statistics?hsLang=en). |
   | --- |
   
     
3. **How do you pronounce µ, the mean of the population, in English?**
   
    a. The way a cow sounds  
    b. The way a kitten sounds  
    c. The way a chicken sounds  
    d. The way a sheep sounds  
    e. The way a bullfrog sounds
   
     
   
   | The correct answer is **b**. For the English pronunciation of **µ** and, more importantly, to understand how the population mean differs from the sample mean, read [this post](https://blog.minitab.com/blog/statistics-tips-from-a-technical-trainer/tip-1-every-sample-statistic-is-a-at-least-little-bit-wrong?hsLang=en). |
   | --- |
4. **What does it mean when we say a statistical test is “robust” to the assumption of normality?**
   
    a. The test strongly depends on having data that follow normal distribution.  
    b. The test can perform well even when the data do not strictly follow a normal distribution.  
    c. The test cannot be used with data that follow a normal distribution.  
    d. The test will never produce normal results.
   
     
   
   | The correct answer is **b**. To find out which commonly used statistical tests are robust to the assumption of normality, see [this post](https://blog.minitab.com/blog/understanding-statistics-and-its-application/what-should-i-do-if-my-data-is-not-normal-v2?hsLang=en). |
   | --- |
   
     
5. **A Multi-Vari chart is used to...**
   
    a. Study patterns of variation from many possible causes.  
    b. Display positional or cyclical variations in processes.  
    c. Study variations within a subgroup, and between subgroups.  
    d. Obtain an overall view of the factor effects.  
    e. All of the above.  
    f. Ha! There’s no such thing as a “Multi-Vari chart!”
   
     
   
   | The correct answer is **e** (or, equivalently, **a**, **b**, **c**, and **d**). To learn how you can use a Multi-Vari chart, see [this post](https://blog.minitab.com/blog/applying-statistics-in-quality-projects/using-multi-vari-charts-to-analyze-families-of-variations?hsLang=en). |
   | --- |
   
     
6. **How can you identify a discrete distribution?**
   
    a. Determine whether the probabilities of all outcomes sum to 1.  
    b. Perform the Kelly-Banga Discreteness Test.  
    c. Assess the kurtosis value for the distribution.  
    d. You can’t—that’s why it’s discrete.
   
     
   
   | The correct answer is **a**. To learn how to identify and use discrete distributions, see [this post](https://blog.minitab.com/blog/adventures-in-statistics/understanding-and-using-discrete-distributions?hsLang=en). For a general description of different data types, click [here](https://blog.minitab.com/blog/understanding-statistics/understanding-qualitative-quantitative-attribute-discrete-and-continuous-data-types?hsLang=en). If you incorrectly answered c, see [this post](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/why-kurtosis-is-like-liposuction-and-why-it-matters?hsLang=en). |
   | --- |
   
     
7. **Which of these events can be modeled by a Poisson process?**
   
    a. Getting pooped on by a bird  
    b. Dying from a horse kick while serving in the Prussian army  
    c. Tracking the location of an escaped zombie  
    d. Blinks of a human eye over 24-hour period  
    e. None of the above.
   
     
   
   | The correct answer is **a**, **b**, and **c**. To understand how the Poisson process is used to model rare events, see the the following posts on [Poisson and bird pooping](https://blog.minitab.com/blog/fun-with-statistics/poisson-processes-and-probability-of-poop?hsLang=en), [Poisson and escaped zombies](https://blog.minitab.com/blog/quality-data-analysis-and-statistics/poisson-rates-and-the-undead?hsLang=en), and [Poisson and horse kicks](https://blog.minitab.com/blog/quality-data-analysis-and-statistics/no-horsing-around-with-the-poisson-distribution-troops?hsLang=en). |
   | --- |
   
     
8. **Why should you examine a Residuals vs. Order Plot when you perform a regression analysis?**
   
   a. To identify non-random error, such as a time effect.  
    b. To verify that the order of the residuals matches the order of data in the worksheet.  
    c. Because a grumpy, finicky statistician said you have to.  
    d. To verify that the residuals have constant variance.
   
     
   
   | The correct answer is **a**. For examples of how to interpret the Residuals vs Order plot in regression, see the following posts on [snakes and alcohol](https://blog.minitab.com/blog/the-statistics-game/snakes-alcohol-and-checking-the-residuals-vs-order-plot-in-regression?hsLang=en), [independence of the residuals](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/violations-of-the-assumptions-for-linear-regression-day-2-independence-of-the-residuals?hsLang=en), and [residuals in DOE](https://blog.minitab.com/blog/statistics-for-lean-six-sigma/residual-revelations?hsLang=en). |
   | --- |
   
     
9. **The Central Limit Theorem says that...**
   
   a. If you take a large number of independent, random samples from a population, the distribution of the samples approaches a normal distribution.  
    b. If you take a large number of independent, random samples from a population, the sample means will fall between well-defined confidence limits.  
    c. If you take a large number of independent, random samples from a population, the distribution of the sample meansapproaches a normal distribution.  
    d. If you take a large number of independent, random samples from a population, you must put them back immediately.  
     
   
   | The correct answer is c, although it is frequently misinterpreted as a. To better understand the central limit theorem, see this [brief, introductory post](https://blog.minitab.com/blog/understanding-statistics/how-the-central-limit-theorem-works?hsLang=en) on how it works, or [this post](https://blog.minitab.com/blog/michelle-paret/explaining-the-central-limit-theorem-with-bunnies-and-dragons-v2?hsLang=en) that explains it with bunnies and dragons. |
   | --- |
   
    
10. **You notice an extreme outlier in your data. What do you do?**
    
     a. Scream. Then try to hit it with a broom.  
     b. Highlight the row in the worksheet and press \[Delete\]  
     c. Multiply the outlier by e-1  
     d. Try to figure out what’s going on  
     e. Change the value to the sample mean  
     f.  Nothing. You’ve got bigger problems in life.  
      
    
    | The correct answer is **d**. Unfortunately, **a**, **b**, and **f** are common responses in practice. To see how to use brushing in Minitab graphs to investigate outliers, see [this post](https://blog.minitab.com/blog/statistics-and-quality-improvement/how-to-use-brushing-to-investigate-outliers-on-a-graph?hsLang=en). To see how to handle extreme outliers in a capability analysis, click [here](https://blog.minitab.com/blog/the-statistical-mentor/how-to-handle-extreme-outliers-in-capability-analysis-v2?hsLang=en). To read about when it is and isn't appropriate to delete data values, see [this post](https://blog.minitab.com/blog/understanding-statistics/can-i-just-delete-some-values-to-reduce-the-standard-variation-in-my-anova?hsLang=en). To see what it feels like, statistically and personally, to be an outlier, click [here](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/my-life-as-an-outlier?hsLang=en). |
    | --- |
    
      
11. **Which of the following are true statements about the Box-Cox transformation?**
    
     a. The Box-Cox transformation can be used with regression analysis.  
     b. You can only use the Box-Cox transformation with positive data.  
     c. The Box-Cox transformation is not as powerful as the Johnson transformation.  
     d. The Box-Cox transformation transforms data into 3-dimensional cube space.  
      
    
    | **a**, **b**, and **c** are true statements. To see how the Box-Cox uses a logarithmic function to transform non-normal data, see [this post](https://blog.minitab.com/blog/applying-statistics-in-quality-projects/how-could-you-benefit-from-a-box-cox-transformation?hsLang=en). For an example of how to use the Box-Cox transformation when performing a regression analysis, see [this post](https://blog.minitab.com/blog/statistics-and-quality-improvement/see-how-easily-you-can-do-a-box-cox-transformation-in-regression?hsLang=en). For a comparison of the Box-Cox and Johnson transformations, see [this post](https://blog.minitab.com/blog/quality-data-analysis-and-statistics/transformers-normal-data-in-disguise?hsLang=en). |
    | --- |
    
      
12. **When would you use a paired t-test instead of a 2-sample t-test?**
    
     a. When you don’t get significant results using a 2-sample t test.  
     b. When you have dependent pairs of observations.  
     c. When you want to compare data in adjacent columns of the worksheet.  
     d. When you want to analyze the courtship behavior of exotic animals.  
      
    
    | The correct answer is **b**. For an explanation of the difference between a paired t test and a 2-sample t-test, click [here](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/t-for-2-should-i-use-a-paired-t-or-a-2-sample-t?hsLang=en). |
    | --- |
    
      
13. **Which of these are common pitfalls to avoid when interpreting regression results?**
    
     a. Extrapolating predictions beyond the range of values in the sample data.  
     b. Confusing correlation with causation.  
     c. Using uncooked spaghetti to model linear trends.  
     d. Adding too much jitter to points on the scatterplot.  
     e. Assuming the R-squared value must always be high.  
     f. Treating the residuals as model errors.  
     g. Holding the graph upside-down.  
      
    
    | The correct answers are **a**, **b**, and **e**. To see an amusing example of extrapolating beyond the range of sample data values, click [here](https://blog.minitab.com/blog/data-analysis-and-quality-improvement-and-stuff/it-was-the-best-of-times-it-was-the-end-of-times?hsLang=en). To understand why correlation doesn't imply causation, see [this post](https://blog.minitab.com/blog/understanding-statistics/no-matter-how-strong-correlation-still-doesnt-imply-causation?hsLang=en). For another example, using NFL data, click [here](https://blog.minitab.com/blog/the-statistics-game/correlation-is-not-causation-why-running-the-football-doesnt-cause-you-to-win-games-in-the-nfl?hsLang=en), and for yet another, using NBA data, click [here](https://blog.minitab.com/blog/statistics-and-quality-improvement/correlation-causation-and-remorse-for-my-nba-finals-prediction?hsLang=en). To understand what R-squared is, see [this post](https://blog.minitab.com/blog/statistics-and-quality-data-analysis/r-squared-sometimes-a-square-is-just-a-square?hsLang=en). To learn why a high R-squared is not *always* good, and a low R-squared is not *always* bad, see [this post](https://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit?hsLang=en). |
    | --- |
    
      
14. **Which of the following are terms associated with DOE (design of experiment), and which are terms associated with a BUCK? **
    
     a. Center point  
     b. Crown tine  
     c. Main effect  
     d. Corner point  
     e. Pedicle  
     f.  Split plot  
     g. Block  
     h. Burr  
     i. Main beam  
     j. Run  
      
    
    | The design of experiment (DOE) terms are **a**, **c**, **d**, **f**, **g**, and **j**. The parts of a buck's antlers are b, e, and h. The Minitab blog contains many great posts on DOE, including several step-by-step examples that provide a clear, easy-to-understand synopsis of the process to follow when you create and analyze a designed experiment in Minitab. Click [here](https://blog.minitab.com/blog/design-of-experiments-2?hsLang=en) to see a complete compilation of these DOE posts. |
    | --- |
    
      
15. **Which of these are frequently cited as common statistical errors?**
    
    a. Assuming that a small amount of random error is OK.  
     b. Assuming that you've proven the null hypothesis when the p-value is greater than 0.05.  
     c. Assuming that correlation implies causation.  
     d. Assuming that statistical significance implies practical significance.  
     e. Assuming that inferential statistics is a method of estimation.  
     f. Assuming that statisticians are always right.  
      
    
    | The correct answers are **b**, **c**, and **d**. To see common statistical mistakes you should avoid click [here](https://blog.minitab.com/blog/real-world-quality-improvement/common-statistical-mistakes-you-should-avoid?hsLang=en). And [here](https://blog.minitab.com/blog/understanding-statistics/three-dangerous-statistical-mistakes?hsLang=en). |
    | --- |

## Looking for more information? Try the online Minitab Topic Library

For more information on the concepts covered in this quiz—as well as many other statistical concepts—check out the [Minitab Topic Library](http://support.minitab.com/en-us/minitab/17/topic-library/topic-library-overview/).

On the Topic Library Overview page, click **Menu** to access topic of your choice.  
 For example, for more information on interpreting residual plots in regression analysis, click **Modeling Statistics > Regression and correlation > Residuals and residual plots.**

[View full post](https://blog.minitab.com/en/blog/statistics-and-quality-data-analysis/the-minitab-blog-quiz-test-your-stat-smarts)

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