The Science Club Wants You to #TakeASample

IRA FLATOW: That noise, that noise can mean only one thing, and that means our Science Club is back in session. And it’s a good time for it, because tomorrow is National Citizen Science Day. And we have the return of our Science Club back with a new project for you to try and our Science Club founders Ariel Zych and Charles Bergquist here. Welcome back.

ARIEL ZYCH: Hello.

IRA FLATOW: What are we working on this month?

ARIEL ZYCH: This month we’re asking all of you listeners to answer a question about something by sampling it.

IRA FLATOW: Sampling it.

ARIEL ZYCH: Sampling it. We want you to take a sample. So this is the take a sample Science Club. We want you to actually use a tiny portion of a thing to understand a larger phenomenon. It’s something that scientists do all the time.

IRA FLATOW: Charles, you want to compound on that?

CHARLES BERGQUIST: Yes. So we’ve got a little challenge for you, Ira, here.

IRA FLATOW: How about just a second, [INAUDIBLE]?

CHARLES BERGQUIST: And, you know, because we know you might be getting a little hungry, we brought some snacks here.

IRA FLATOW: Wow.

CHARLES BERGQUIST: Here we go.

IRA FLATOW: M&Ms, filling the vase with M&Ms.

ARIEL ZYCH: I love that sound.

CHARLES BERGQUIST: So your challenge, Ira, is was to tell me how many red M&Ms are in that jar.

IRA FLATOW: Oh my goodness. I have no idea.

CHARLES BERGQUIST: So this is something. You know, it’s a pretty big vase. And it’s a pretty big bag of M&Ms. And so it would be a real pain to have to go through and count all these one by one.

IRA FLATOW: I volunteer to try and eat that.

CHARLES BERGQUIST: Well, you can work on that on your time. But if we take a sample of it, maybe pull a random handful of, say, 10 M&Ms, and see how many red ones there are in that handful, we’ll probably want to do that a couple times just to make sure that we didn’t randomly pull out some unrepresentative sample, where I happened to grab all yellow ones or something. But if you do that a couple times, you can then extrapolate and make judgments about what might be in the rest of the vase.

IRA FLATOW: So if I wanted to figure out they were all different colored M&Ms in there and how many were yellow, how many were green, blue, I could take a sample of it?

ARIEL ZYCH: You got it.

IRA FLATOW: And that’s what you’re asking our listeners to do?

ARIEL ZYCH: Exactly.

IRA FLATOW: Not just with M&Ms, but anything they wanted to?

ARIEL ZYCH: Anything you’re curious about. So if you’ve got a question about the world around you, if you got a question about the world inside you, frankly, samples are the way to get at that question. What’s terribly cool about sampling is that you don’t actually need to count all the stuff or observe everything. You can take a sample and answer a question that otherwise would just be way too difficult or way too hard to observe exhaustively.

So for example, most of you guys have probably already sampled things in your lifetime without realizing you were doing it. If you’ve gone to the grocery store and you pick up a melon and you’re trying to figure out if that’s a good melon, you have a sense about what’s a good melon by this point, Ira, right?

IRA FLATOW: Absolutely.

ARIEL ZYCH: So that’s because every time you’ve gone to the store and picked out a melon, you’ve looked at that melon and tried to figure out, from that one melon sample, what makes a delicious melon? So you’ve been sampling, and you use that sample to figure out more about the melons of the world.

IRA FLATOW: I’m Ira Flatow. This is Science Friday from PRI Public Radio International. Here with our Science Club, Ariel Zych and Charles Bergquist, talking about this month’s challenge is sampling. And yes, you’re right, we have all in our lifetimes started to sample on this, but this time you want us to do it systematically.

CHARLES BERGQUIST: We want you to pick out something in the world that you’re curious about that would take a lot of work to figure out in a straightforward manner. How many hairs are on my dog?

ARIEL ZYCH: Your poor dog.

CHARLES BERGQUIST: You can’t count that.

ARIEL ZYCH: It would be terrible.

CHARLES BERGQUIST: Maybe you want to know something about the way traffic goes past your house.

IRA FLATOW: Oh, so you could sample something in time rather than objects.

CHARLES BERGQUIST: Rather than sit there all day for an entire week and count every single car that goes by your house, you could tell yourself I’m going to take a picture of the intersection every half hour. And yeah, it’s not going to be a perfect representation of it, but it will let you arrive at some estimates and make some conclusions about what the rest of the whole might be like.

IRA FLATOW: So you can sample how many Chevy, Fords, and Teslas go by in 10 minutes and see how many you think might go by during the day or so.

ARIEL ZYCH: You’ve got it. And you’re probably going to figure out, as you guys have inferred, that maybe it depends on where you sample or how you sample what kind of answer you’re going to get. So the number of Teslas that drive past your house might be different in different parts of the country than another.

IRA FLATOW: Now that’s for sure.

ARIEL ZYCH: I mean, similarly like if you wanted to go, say, weigh the average mass of a dog, Charles, right?

CHARLES BERGQUIST: Yeah, you wouldn’t go to the Great Dane club owners meeting or something like that.

ARIEL ZYCH: Or go to chihuahuas anonymous, like that would not be a way to get an unbiased sample of, you know, dog mass. So then take your sampling protocol into consideration. Think very critically as you answer your question with sampling.

IRA FLATOW: Are we aiming this at anybody, everybody?

ARIEL ZYCH: Everybody and everybody.

IRA FLATOW: Teachers, students.

ARIEL ZYCH: Teachers.

IRA FLATOW: Scientists?

ARIEL ZYCH: Scientists. Yes, scientists, especially. Scientists send us your sweaty weird field sampling photos and tell us what you were doing with those field samples. If you’re a lab and you have samples as part of your lab and you do work on tissue samples or animal samples, whatever your thing is, tell us about it. Because we want to see how you use sampling, and then we want everybody else to go try it themselves.

IRA FLATOW: And do we have any rules to follow?

ARIEL ZYCH: There are some rules I would recommend following. So please be lawful in your pursuits. Be respectful. Don’t go trampling and sampling in private property. Try to sample things with their consent, which is code for, like, please don’t accost animals or other living organisms and don’t ruin them. But actually, otherwise, it’s pretty open.

IRA FLATOW: And how do they communicate with us about their sample?

CHARLES BERGQUIST: So we’re taking submissions online in all kinds of social ways. We’re on Twitter. We’re on Facebook. We’re on Instagram. Our hashtag for the project is take a sample. And you can find complete directions on all the ways that you can get in touch with us on our website at sciencefriday.com/scienceclub.

ARIEL ZYCH: You got it.

CHARLES BERGQUIST: Can they send us little videos of what they’re doing?

ARIEL ZYCH: Please do. I would love sampling videos. That’d be awesome. I want to see what you sampled. I want to know what question you’re trying to answer with your sample. I want the nitty-gritty, no matter how ambitious or how subdued. It if you just want to know, you know, how many hairs on your dog, I mean, that’s a cool sample question.

CHARLES BERGQUIST: And because in science failure is always an option. We’d like to see if you know how well you’re doing stuff, because we learn from failure.

ARIEL ZYCH: Absolutely. And I’ve had my time with some bad sampling in my day. I mean, there are some people that have to sample some really unfortunate things, like people who study scat go into the field and they study the samples left by animals. And it’s an incredibly powerful way to learn about animal ecology and foraging patterns. It might not be glorious, but it is informative.

CHARLES BERGQUIST: And you know, if you’re a professional and you do this all the time, yes, we know that there’s a lot of hoops that you have to jump through to make your sample to be statistically relevant and make those numbers actually tell you an accurate story. And so if you’re a pro, please tell us how you’re doing that too.

IRA FLATOW: That’s great. And we’ll meet back here May 13 in a month?

ARIEL ZYCH: Yes. You have a whole month to do some sampling. Check out our website. We’ve got even more projects that you can sample with as part of Citizen Science Day, thanks to SciStarter.

IRA FLATOW: There you go. That’s our project for the month. Ariel Zych and Charles Bergquist, thank you.

ARIEL ZYCH: Awesome. Thanks a lot.

CHARLES BERGQUIST: Thanks, Ira.

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