It’s finally time to run our study!
After all of the hard work our Citizen Scientists have put into creating, designing, and testing their ideas, Week 7 is the moment of truth.
Before we get into that, even though we’re nearing the end of this summer’s project, we’d still love to have you sign up officially if you want to. That way you can be on our leaderboard or help us better understand how to do citizen science better! Feel free to keep voting or commenting without signing up, though – we’re just happy you’re here!
This week, we’re sending off the study for real people to participate in! We also want to give you one last chance to make some guesses about what might happen in our experiment.
What do you think we’re going to see? We have a couple questions for you about what the answer might be to our research question, and about how different people might do differently in the study’s task. Thinking about these kinds of things is fun, and it helps us be more aware of our biases before we start evaluating our results.
Last week, we asked you to help us test out our stimuli and see if you could name our pictures. Everyone did great, and most of the pictures were consistently named! Thanks again to everyone who tried it out. There were only a couple that had some different answers, like DVD vs. CD and hippo vs. hippopotamus. The nice thing is that these responses still show that the pictures are recognizable, and as long as participants in the study can prove they remembered a picture, it doesn’t really matter what they’re called exactly!
Now, let’s go over how we are going to get our data in our final experiment.
All of this is going to be run through a service called Prolific. We just have to make an online study, and then we can send it off to be taken by participants all over. We can specify the kinds of people who should be taking our study, like if we need only adults or those who speak a certain language, and we can pay them a fair rate for their time. If you have any friends or family who you think might want to take our survey, they can make an account on Prolific and be in all kinds of studies too!
That being said, though we’re sure you’re curious, none of our Citizen Scientists can be in the study you helped us to design. It’s for the same reason that none of us on the BLNDIY research team can participate, either: we know too much! Even if you wouldn’t mean to, taking the survey when you know the goals of the study can influence our results, especially if you expect or desire a certain outcome. That’s why we only tell participants so much about an experiment until after they finish it. We only want them to know enough to be able to do the task we’re giving them and feel safe while doing so. If we said beforehand exactly what we’re looking for, then it wouldn’t be a controlled experiment like good science should be!
Bias is something that’s really important to consider in experiments, which we’ve seen in a couple steps of our experimental designing that we’ve done here. A really cool example of this is the Clever Hans effect. Here’s a short video telling the story of a horse that appeared to be able to do math. It turns out that the humans testing Hans accidentally gave him clues to answering arithmetic problems without even knowing they were affecting the results at all… (And here’s an extra article if you want to know more!)
That’s it for this week!
It won’t be long before we have some real data for your experiment.
Again, CLICK HERE to make some bets on what might happen in the experiment.
And don’t forget our Language Fun section on the site! If you haven’t checked it out yet, we have all kinds of cool games and quizzes that are all about language science.
Are you excited? Share what you think is gonna happen down in the comments! Will people remember more words in the out-loud condition, the in-your-head condition, or will it be about the same between the two? If there’s a difference, how big of a difference will it be?
Next week, we’ll have some preliminary results to share, and we’ll talk about making qualitative observations of our data.
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