Pocket Data Science

Pocket Data Science for Locked Down Kids (and Adults!)
Who has better pockets? And who has the most pockets? Is Pocket Inequality a real thing?
For this project you need to measure the pockets in clothes around the house, and figure out whose clothes have better pockets.
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Age level:
You can tailor this to any age group, but it’s aimed at Primary School kids (so roughly 5-12). Younger kids might need help with the measuring and the angles, and the writing.
You can leave out steps that are too tricky, and simplify the data analysis (or make it harder!) depending on where your kids are up to with maths and spreadsheets.

* Pen and Paper or laptop with spreadsheet.
* A pile of clothes.
* A tape measure or ruler.
You might want to choose which clothes to use for the study – for example, maybe limit your kids to clothes in the washing hamper, so that they’re not pulling every item of clothing out of your wardrobe and spreading them all over the house!

Step 1
What kinds of pockets do your clothes usually have? Do you think some types of clothing have better pockets than others? Is there a gender bias in pockets? Tell your family, or write down what you think you are likely to find when you count and measure the pockets in different types of clothing.

Step 2
Gather a range of different types of clothing from different members of your family. You might like to use the washing hamper, if your family all puts their washing in one place, or pick out a few items of clothing from everyone in your family.

Step 3
For each item of clothing, fill out this form:

Or make a table and fill it with answers to these questions.
You can use a copy of the spreadsheet provided. You might like to print it out!

Age group: Is the clothing intended for
* Babies/toddlers
* Kids
* Adults

Gender: Is the clothing intended for
* Women
* Men
* Unisex
* Can’t tell

What type of clothing is it?
* dress
* jacket/coat
* long pants
* shorts
* skirt
* shirt/top/t-shirt
* Other:_____________

How many pockets are there in this piece of clothing?

For each type of pocket, answer these questions (for example, a pair of jeans might have two front pockets and two back pockets, a pair of cargo pants might have hip pockets and deep ones further down, etc):

How many pockets just like this are there in this piece of clothing?

What is the angle of the pocket opening? (for “other” use your best guess at the angle from vertical)
* Horizontal (parallel to the floor)
* Vertical (straight up and down)
* 45 degrees (half way between horizontal and vertical)
* Other:

Does the pocket have a closure of some kind?
* Zip
* Velcro
* Button
* Press stud
* Flap without a fixture
* No
* Other_______________________

How long is the pocket opening, in centimetres?

How deep is the pocket, in cm, from the lowest part of the opening to the bottom of the pocket?

How wide is the pocket at its widest point?

How likely are things to fall out of this pocket during normal activities, eg walking, sitting down, standing up, etc. Just give us your best guess.

(Use a number from 1 to 5, where 1 means Extremely likely, happens all the time, and 5 means Extremely unlikely, almost never happens unless I am upside down)

Step 4
Take a look at your data. How does it look? Which type of clothing do you think has more pockets? Which has deeper pockets? Are most pockets likely to have things fall out of them, or does it look like most of them are good?

Step 5
For this step you can just use your own data, or you can download the full dataset here:

Organise your data into results for mens’ clothing, womens’ clothing, and unisex clothing (If you don’t have much data for one category, just use the two you do have data for).

Make a bar chart of pocket depth for each category.
Younger kids can do this by stacking blocks. Eg, if a pocket is 10cm deep, stack 10 blocks on top of each other. For a pocket that’s 5, stack 5. Etc. Do that for five items of at least two categories of clothing. (If you don’t have enough data to do that, you can grab some from the dataset above).
Older kids can draw the graphs by hand, or using a spreadsheet package.

Which graph looks taller? Whose pockets are deeper?
Make some more creative pictures that show the differences in the data in an interesting way.

If you’re really proud of your picture, send a copy to contact@adsei.org and we’ll share it on our website!

Step 6 (for kids who are ready for averages)
So who wins? We can work out the average of a set of numbers by adding them up and dividing them by how many there are. So, for example, if you have 5 pocket depths: 6, 9, 10, 5, and 8, the average would be 6+9+10+5+8 divided by 5, which is 7.6.

Calculate the average number of pockets for each of the three categories of clothing: Mens’, Womens’, and unisex. Now calculate the average depth of pockets for each category.

This next one is a bit different – we collected a rating for each pocket of how likely things were to fall out of it using a scale of 1 to 5, where 1 means Extremely likely, happens all the time, and 5 means Extremely unlikely, almost never happens unless I am upside down. Calculate the average likelihood of things to fall out of the pockets for each of the three categories of clothing. Remember that a value closer to 1 means things are more likely to fall out, and a value closer to 5 means things are less likely to fall out. Who wins for this value? What value do you think is winning here? A lower number or a higher one?

Step 7
Exploratory questions.
* How accurate do you think your measurements were?
* How accurate do they need to be to compare the different categories of data?
* Does the average tell you everything you need to know about this data?
* What about the maximum and minimum values?
* How many items of clothing had no pockets at all?
* Was there a difference between male, female, and unisex clothing for no pockets?
* What was the maximum number of pockets?
* Might there have been data that could not have been collected by this form? IE other questions that should have been asked?

There is so much to explore in this dataset, it has great potential for an open ended classroom exercise. Download it as a csv and play with it in Python. Make graphs in Excel/Numbers/Google Sheets and consider readability, labelling, axes, etc. Compare different parts of the data. Share back the ways you explore this data – either to contact@adsei.org or in the Teachers Using Data Science Facebook group and I will post them here.

If you’d like to fund ADSEI to build full lesson plans with rubrics, curriculum links, and resources, get in touch!