Jeff Sauro • June 26, 2012

What do you think the most common question in statistics is?Several times a year I teach a statistics course for UX professionals and get asked this question a lot.

We're offering the class this fall at the LeanUX Denver conference and a portion of it is available for download.

Some attendees have had statistics classes and for others it's their first one. Regardless of the background, almost everyone who uses statistics wants to know: What statistical procedure do I use?

It's hard enough to grasp many of the concepts in statistics. Most people in UX aren't math majors and never intended to use statistics as part of their job.

For this reason we have a decision tree to help you know when to use which statistical procedure in both the Excel calculator and in Chapter 2 of our book Quantifying the User Experience.

Getting to know the decision map is one of the most popular parts of the course because you can click right to the appropriate calculator after answering a couple questions, paste your data and get your answer.

I've included a similar clickable version below with links to several of our free online calculators. To use the decision map you just need to know a few things.

- Binary (pass/fail, yes/no, purchase/didn't purchase) and coded as 1 or 0. These include things like completion rates and conversion rates.
- Continuous: If your data isn't coded as 1's or 0's then we can usually treat it as what's called continuous or metric data.

The blue boxes at the termination points on the two decision maps below link to our free online calculator and if I don't have one up yet (more coming soon!) it will link to the Excel calculator.

You can also download a free high-resolution printable version [3.5MB pdf] of both maps. Pin it to your wall for help or throw darts at it if statistics just frustrates you too much.

- It's not a binary measure so we'd use the continuous decision map.
- We aren't comparing data, we only want to know how precise our estimate is so we pick "N" in the first branch.
- We don't have a benchmark we're testing against (such as testing whether the average exceeds 3.5) so we again pick "N".
- The data isn't task time so we pick "N" and end up at the t confidence interval link. Entering in the mean, standard deviation and sample size as summary data gets us a 95% confidence interval from 3.494 to 4.306.

- Completion rates are a binary measure (pass/fail) so we'd use the binary decision map.
- We ARE comparing groups—we want to know if users on different websites will have different completion rates when finding a location so we pick the "Y" in the first branch.
- We have different users in each group (called Between Subjects) so we pick "Y" again.
- We have only 2 groups so we pick "N" at the 3 or more groups box.
- We end at the N-1 Two Proportion Test with a link to the A/B Test calculator link. Entering 12 out of 12 and 17 out of 24 gets us a p-value of .03985, which means the difference is statistically significant.

- Completion rates are a binary measure (pass/fail) so we'd use the binary decision map.
- We are not comparing groups so we pick the "N" in the first branch.
- We ARE testing against a benchmark of 75% so we pick "Y" again.
- We end at the 1 Sample Binomial Test with a link to the One Proportion Calculator. Entering 20 out of 25, "Is Greater Than" and a Test Proportion of .75 tells us there's about a 70% chance at least 75% of all users would be able to find the Sewing Maching--not terribly compelling evidence.

- SUS scores are not binary so we'd use the continuous decision map.
- We ARE comparing groups so we pick the "Y" in the first branch.
- We have different users in each group (called Between Subjects) so we pick "Y" again.
- We have only 2 groups so we pick "N" at the 3 or more groups box.
- We end at the 2 Sample t test with a link to the online calculator. We enter the summarized values and get a p-value of .0962 which means there's good, although not overwhelming evidence, that the difference is statistically significant.

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5 Examples of Quantifying Qualitative Data

10 Things to Know about Usability Problems

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Customer Analytics for DummiesA guidebook for measuring the customer experience Buy on Amazon | |

Quantifying the User Experience: Practical Statistics for User ResearchThe most comprehensive statistical resource for UX Professionals Buy on Amazon | |

Excel & R Companion to Quantifying the User ExperienceDetailed Steps to Solve over 100 Examples and Exercises in the Excel Calculator and R Buy on Amazon | Download | |

A Practical Guide to the System Usability ScaleBackground, Benchmarks & Best Practices for the most popular usability questionnaire Buy on Amazon | Download | |

A Practical Guide to Measuring Usability72 Answers to the Most Common Questions about Quantifying the Usability of Websites and Software Buy on Amazon | Download |

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