Week 9
This week, I delved into deep biases that can exist in personas. I read some good arguments for removing demographics (age, gender, ethnicity and location) from your personas. But wait, before you get after me, you're not going to throw the whole persona out. When you really take a deep look at these specifications what are they adding to your persona?
For example, what does age have to do with how well your user will do on the app? If you reply, "Well, older people aren't as tech savy," you are showing me your bias. That isn't always the case, they have as much capability to be tech savy as a younger person. Describing their motivation would do you better. Gender? There are more variations within the sexes than between. Race as well. If you're only including race to show your diversity that you've included someone with brown skin, you're missing the point. Culture, on the other hand can be valuable to include. The only time that gender and race can truly aid you, is if you are testing something that is explicitly involving gender OR to intentionally drive thinking away from what tends to be many people's default thought of a white, male user. The last demographic that I argue to toss out is location. The environment does not equal the persona. Many situations are relevant to people that live in all sorts of places. The only time this is actually relevant is if say you're researching people who grow food in urban environments vs those who grow them in rural. There, the environment would have an effect on your research.
You also need to make sure that what you're labeling or naming your personas is not biasing your team against your persona. People are fluid and change when they're that persona all the time. You don't want to label your persona anything that that group of people wouldn't be proud to be labeling. For example, labeling a persona as "The grumbler" vs "The frustrated". Grumbler implies that the person is being annoying and is perhaps in the wrong. Frustrated, implies that the person is justifiably frustrated with some need that is not being met. The negative connotation is removed.
I hope you enjoyed these few suggestions on how to remove biases from your personas through removing demographics.
For example, what does age have to do with how well your user will do on the app? If you reply, "Well, older people aren't as tech savy," you are showing me your bias. That isn't always the case, they have as much capability to be tech savy as a younger person. Describing their motivation would do you better. Gender? There are more variations within the sexes than between. Race as well. If you're only including race to show your diversity that you've included someone with brown skin, you're missing the point. Culture, on the other hand can be valuable to include. The only time that gender and race can truly aid you, is if you are testing something that is explicitly involving gender OR to intentionally drive thinking away from what tends to be many people's default thought of a white, male user. The last demographic that I argue to toss out is location. The environment does not equal the persona. Many situations are relevant to people that live in all sorts of places. The only time this is actually relevant is if say you're researching people who grow food in urban environments vs those who grow them in rural. There, the environment would have an effect on your research.
You also need to make sure that what you're labeling or naming your personas is not biasing your team against your persona. People are fluid and change when they're that persona all the time. You don't want to label your persona anything that that group of people wouldn't be proud to be labeling. For example, labeling a persona as "The grumbler" vs "The frustrated". Grumbler implies that the person is being annoying and is perhaps in the wrong. Frustrated, implies that the person is justifiably frustrated with some need that is not being met. The negative connotation is removed.
I hope you enjoyed these few suggestions on how to remove biases from your personas through removing demographics.
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