When conducing design research we all want our outcomes to be significant. In other words, we want results to be valid, trustworthy, and able to be acted upon with confidence. Here’s where statistics can be our friend. When design researchers are equipped with basic statistics knowledge and an ability to apply these concepts, they can make sense of findings and decide with confidence if they can be used to base design decisions upon. For instance…

If we designed a watch that could predict the future, and we were to pitch the design to a company who would like to buy it, do you think they’d be more likely to buy it if it worked 4 our of 5 times or 4,000 out of 5,000 times we tested it? What if we defined “it worked” as 51% “working” versus 99% “working”? My guess is we’d have a better shot if it 99% worked 4,000 out of 5,000 tests.

If that’s not convincing, here’s a TED talk by Alan Smith titled *Why you should love statistics* that says it far better than I ever could.

Ready for more? Here are a few videos to walk you through statistics concepts from basic down to fairly complex.

## Practical vs. Statistical Significance

Practical and statistical significance are different but both useful. Don’t take my word for it, Spider-Man knows what he’s talking about.

## Statistical Vocabulary

Let’s get our feet under us with a little statistical vocabulary.

## Introduction to Statistics

Want to know why statistics matters for design research? This video helps clarify how statistics aren’t just numbers, but they’re about the behaviors and trends statistics represent.

## Basics: Calculating Average

A lot of times, when we’re reporting statistics in research we have to figure out averages to capture the probability or percentage of something happening. Here’s a refresher on calculating average in various ways.

## Statistics Basics: Concepts and Terminology

Wading into statistics means we have to use some potentially unfamiliar terms for designers. This video makes these terms much clearer, including why they’re important.

## An Applied Statistics Example

Probability is one of the most common payoffs of statistics… we want to know what will happen in the future based on what we found via research. This video walks through the process of applying statistics in an experiment.

## P-Value

You’ll likely hear the term “P-Value” thrown around the closer you get to statistics and quantitative data. Here’s a good introduction to P-Value.

## Go Big

Take time to understand statistics. If you report any kind of percentages or numbers that represent your findings, knowing what you’re talking about will be imperative. As you work in the mixed methods world of design research, using statistics will reinforce your recommended design decisions.