Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Well done!
You have completed Data Analysis Basics!
You have completed Data Analysis Basics!
Preview
Presenting your findings is a key part of any analysis. In this video we'll talk about how you should present your findings and go over some potential pitfalls!
This video doesn't have any notes.
Related Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign upRelated Discussions
Have questions about this video? Start a discussion with the community and Treehouse staff.
Sign up
Once you've found something in the data,
0:00
you probably want to start
telling your co-workers.
0:02
But before you go telling your
results to everyone you know,
0:04
there's one very important
thing you need to know.
0:07
Correlation does not imply causation.
0:10
Here is a graph of ice cream
sales versus crime rates.
0:13
It's clear that higher ice cream sales
correlates with higher crime rates.
0:17
But that doesn't mean that higher
ice cream sales causes more crime.
0:21
That'd be ridiculous.
0:24
When you're presenting your findings,
you need to remember that even though two
0:27
things might be related, that doesn't mean
they have anything to do with each other.
0:30
The most you can say is
that they're correlated.
0:35
And on that note, I wonder what a graph
of our age data would look like.
0:38
Let's hop back to
Google Sheets to find out.
0:41
To make a graph of ages versus counts,
let's select both the age and
0:45
count columns, including the headers.
0:49
And then let's click on
the Insert Chart button.
0:53
And there we go.
0:57
Thanks to all these spikes, it's clear
clear that some ages are overrepresented.
0:58
Let's drag this chart to the top.
1:03
And then since we don't really need this
legend over here, let's remove it by
1:18
going into the chart editor, clicking the
customize tab, selecting the legend and
1:23
for position, set it equal to none
to remove the legend from the chart.
1:28
Perfect.
1:33
One thing you want to
be careful of though,
1:35
is the charts can be
unintentionally misleading.
1:36
Having all that data
represented graphically
1:39
is a lot noisier than having it in text.
1:42
If you're trying to report something,
1:45
don't rely on others to
interpret your graphs.
1:46
Make it easy for them by spelling
out exactly what you found.
1:49
A good model to follow when explaining
your findings is the outline of
1:53
a scientific article.
1:56
Start with an introduction where
you introduce the problem and
1:58
how it came to be.
2:01
Then, formally state the hypothesis and
2:03
describe the procedure used
to test that hypothesis.
2:06
Finally, you want to report
the results of your testing and
2:09
then share any conclusions.
2:13
Here's what this might look like for
the problem we just tackled.
2:15
Introduction, we've received
complaints that some ages
2:19
have an easier time
qualifying than others.
2:22
We aim to assess the truthiness
of those claims.
2:25
Hypothesis, some ages have an easier
time qualifying than others.
2:28
Procedure, to find this out, we looked
at the number of qualifying runners for
2:33
adjacent ages.
2:37
If no ages have an advantage,
2:39
the difference between adjacent
ages should be relatively small.
2:41
We picked a figure for what would
be an unacceptable difference, and
2:44
then tested adjacent ages.
2:48
Results, we found four differences
that exceeded our maximum.
2:50
Conclusions, we conclude that some
ages have an easier time qualifying
2:54
than others.
2:58
Breaking it down this way makes it
easy to understand what's going on.
3:00
There are many things you can
do to present your findings, and
3:04
this is just one way.
3:06
But it's nice to have a format in mind,
at least to get you started.
3:08
Another thing to mention is that unlike
a scientific article, at the end,
3:12
you might want to include
a recommended solution.
3:16
If you think you've found a solution
to the problem, make sure to share it.
3:19
It really helps bring things full circle.
3:22
Also, before we go,
3:25
you should know that we haven't found
anything particularly alarming here.
3:26
The Boston Marathon uses different
qualifying times for different age groups.
3:30
So it's sort of an open secret that
you'll have an easier time qualifying
3:34
as a 35 year old instead of a 34 year old.
3:38
So maybe our recommendation would
be to get rid of age groups and,
3:41
instead, have a different
qualifying time for each age.
3:45
There's so
much you can do with data analysis,
3:49
from figuring out which peanut butter to
buy to finding a good deal on a house,
3:51
data analysis informs our every decision.
3:55
But this is just the beginning, there's
lots more to learn about data analysis.
3:58
Until next time.
4:03
You need to sign up for Treehouse in order to download course files.
Sign upYou need to sign up for Treehouse in order to set up Workspace
Sign up