In this video, learn about the value of testing and when to apply.
- Testing is what many refer to as the fifth stage in the design thinking process. It often follows the prototype stage because suddenly we've got something to test, but it doesn't have to. In fact, it's best to get your ideas in front of real users as often as possible, as early as possible, before you even have a prototype. Why? You might ask. Because user feedback is priceless. Without an understanding of what your customers need in order to achieve their goals, the iterative process will fail. Let's briefly touch on what we mean by iterative process, and why it's so important to design thinking. If you're successful at learning more about the people who use your product, you'll change, adjust, refine, or adapt your perspective. Each time you learn new things, you should be testing new ideas with real users. Since you are refining your design and then testing again, we call this an iterative process. So what do you think may be the main reason we test? So we can change our minds. I'm not kidding. We want to prove ourselves wrong. That's right. We want to invalidate our hypothesis. You may be saying, "Well, Randall, that sounds all good and well, "but what do we do when we determine "that our idea didn't work? "We failed, now what?" Well, I'm glad you asked. That brings us the next important reason we test, to learn. A great way to see the value in this is to imagine you are the teacher. Let's say you gave your students a test. After the test, the only information you were given about the results was who passed and who failed. No other information would be passed onto you. How much would you learn? More importantly, would you have enough information to know how to help those who struggled? Unlikely. You could only tell them their score, and for those who did well, would you know enough to appropriately challenge them? Okay, so I hope you get the point. You need more information. You need more data, but what kind of data? How about some more numbers? Such as 34% of your students misinterpreted question number five, or 72% got the multiple choice, structured questions correct. We call those numbers, quantitative data. It's data, but it's not the data you need to understand the why's. For that, we need qualitative data, or an easier way to think about it, we need to know the why. If you knew why a student struggled with a particular question, you could help them understand. So pulling back to testing and design thinking, we test with real customers, so that we can understand what works and what does not, and understanding the why helps us to come up with another potential solution to test. Simply put, we test to understand, learn, and refine.