Yesterday afternoon the after school teen program I help make content for, Future Science Leaders, participated in SFU’s Young Innovator’s Crawl. Crawlers traveled around the city visiting “innovators” (in art, tech, anything) to see demonstrations, talks, tours, etc.. FSL Fellows (like me) worked with FSL students to make a crawl stop at Science World. There were several tables including a gravity demonstration, 3D printers, and an entomophagy table. At the entomophagy table we tried out a crowd modification of the Buggy Snack Box experiment, rounded out with additional cookies that were all made with mealworm flour so that everyone could taste an entomophagous cookie.
Like the box, the goal of this activity is to combat the preconception of “gross flavour” people sometimes make in association with “insects”. I.e., we’re trying to combat the “Ick Factor”. This time, I’ve scaled the experiment up to be used for an outreach event instead of a snack box for one person. I’ve also made a few small modifications to make the cookies work a bit better.
I made “Chewy Brownie Chip Drops” which are essentially chocolate chocolate-chip cookies with walnuts. The point of changing the recipe was the cocoa powder, which helped mask the colour difference caused by the cricket flour. We still added a bit of food colouring, but much, much less than when I made the peanut butter cookies.
Just like last time, I baked half using a partial replacement of cricket flour, and half 100% white flour. To save on cookie-top real estate, this time I used only 2 Smarties for unique IDs, including orange (more on that another time). That gives me 36 unique IDs; 18 for flour, and 18 for cricket. I reused the same 18 for the same type several times so that I could bake more than 36 cookies but still have 2 Smarties reliably tell me the content.
On the day I displayed several cookies on the table, half of which were cricket and half of which were regular so that attendees would have a 50% chance of selecting a cricket cookie. It would be better to assign cookies to attendee by flipping a coin, just in case the cocoa powder and food colouring didn’t mask the colour difference well enough. However, on the day, this piece of scientific rigor was trumped by how nice it looked (and how much faster it was with groups of people) to have a big spread of cookies. If I do it again and have enough helpers, I’ll do it properly.
I was also careful not to put two of the same colour combination on the table at the same time because then attendees could have guessed that they were the same. I slipped up a few times, but luckily only the FSL student volunteers spotted the discrepancy (as far as I know).
After tasting their cookie, each attendee would guess “Cricket” or “Plain”. One of the FSL students was on patrol continuously updated a simplification of the results on a big pad of paper as “correct guess” and “incorrect guess” bar charts. Next time I will remember my computer and have someone continuously updating and projecting the actual results.
Speaking of results, here they are! Scroll to the bottom of this entry to see how to do the analyses in JMP11, online with GraphPad, or with R.
Based on a Chi-Square (1, n=30)=0.54, p=0.46
Based on Fisher’s Exact test p=0.72
We can say that we have NO EVIDENCE to suggest the Young Innovator’s Crawl attendees who stopped at my table were able to correctly identify cricket flour in cookies.
Take THAT “Ick-Factor”!
If you give this activity a try, please write to me to let me know how it goes!
We want to see whether people attending this event could correctly guess if their cookie had or did not have cricket four in it. Our null is that they cannot tell.
Analyses with JMP 11:
Set your data up like so:
In JMP select Analyze>Fit Y by X. We’d like to know if the Truth about the cookie (made with cricket or plain flour) predicts what people guess, so Response is Guess and Factor (AKA predictor) will be Truth. Remember that this is frequency data.
JMP will generate a Contingency Table, a mosaic plot, and show results of both the chi-squared and Fisher’s Exact test.
Analyses with Graph Pad
You can do the analyses online with Graph Pad, in which case you will need to enter your data into the contingency table as shown. I’m not sure if you can use it to generate your mosaic plots.
Analyses with R:
You will need your data to be a CSV with 1 row per observation (printed below for illustration).
> cookiedata <- read.csv(“YOURDATALOCATION”, header=T, as.is=T)
1 Cricket Cricket
2 Cricket Cricket
3 Cricket Cricket
4 Cricket Cricket
5 Cricket Cricket
6 Cricket Cricket
7 Cricket Cricket
8 Cricket Cricket
9 Plain Cricket
10 Plain Cricket
11 Plain Cricket
12 Plain Cricket
13 Plain Cricket
14 Plain Cricket
15 Plain Plain
16 Plain Plain
17 Plain Plain
18 Plain Plain
19 Plain Plain
20 Plain Plain
21 Plain Plain
22 Plain Plain
23 Plain Plain
24 Cricket Plain
25 Cricket Plain
26 Cricket Plain
27 Cricket Plain
28 Cricket Plain
29 Cricket Plain
30 Cricket Plain
> cookietable <- table(cookiedata)
Guess Cricket Plain
Cricket 8 7
Plain 6 9
> mosaic(cookietable, shade=T, legend=T)
Pearson’s Chi-squared test with Yates’ continuity correction
X-squared = 0.1339, df = 1, p-value = 0.7144