Heads up! To view this whole video, sign in with your Courses account or enroll in your free 7-day trial. Sign In Enroll
Preview
Start a free Courses trial
to watch this video
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!
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 up