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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!
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Once you've found something in the data,
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you probably want to start
telling your co-workers.
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But before you go telling your
results to everyone you know,
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there's one very important
thing you need to know.
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Correlation does not imply causation.
0:10
Here is a graph of ice cream
sales versus crime rates.
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It's clear that higher ice cream sales
correlates with higher crime rates.
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But that doesn't mean that higher
ice cream sales causes more crime.
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That'd be ridiculous.
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When you're presenting your findings,
you need to remember that even though two
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things might be related, that doesn't mean
they have anything to do with each other.
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The most you can say is
that they're correlated.
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And on that note, I wonder what a graph
of our age data would look like.
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Let's hop back to
Google Sheets to find out.
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To make a graph of ages versus counts,
let's select both the age and
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count columns, including the headers.
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And then let's click on
the Insert Chart button.
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And there we go.
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Thanks to all these spikes, it's clear
clear that some ages are overrepresented.
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Let's drag this chart to the top.
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And then since we don't really need this
legend over here, let's remove it by
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going into the chart editor, clicking the
customize tab, selecting the legend and
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for position, set it equal to none
to remove the legend from the chart.
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Perfect.
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One thing you want to
be careful of though,
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is the charts can be
unintentionally misleading.
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Having all that data
represented graphically
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is a lot noisier than having it in text.
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If you're trying to report something,
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don't rely on others to
interpret your graphs.
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Make it easy for them by spelling
out exactly what you found.
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A good model to follow when explaining
your findings is the outline of
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a scientific article.
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Start with an introduction where
you introduce the problem and
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how it came to be.
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Then, formally state the hypothesis and
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describe the procedure used
to test that hypothesis.
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Finally, you want to report
the results of your testing and
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then share any conclusions.
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Here's what this might look like for
the problem we just tackled.
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Introduction, we've received
complaints that some ages
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have an easier time
qualifying than others.
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We aim to assess the truthiness
of those claims.
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Hypothesis, some ages have an easier
time qualifying than others.
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Procedure, to find this out, we looked
at the number of qualifying runners for
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adjacent ages.
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If no ages have an advantage,
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the difference between adjacent
ages should be relatively small.
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We picked a figure for what would
be an unacceptable difference, and
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then tested adjacent ages.
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Results, we found four differences
that exceeded our maximum.
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Conclusions, we conclude that some
ages have an easier time qualifying
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than others.
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Breaking it down this way makes it
easy to understand what's going on.
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There are many things you can
do to present your findings, and
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this is just one way.
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But it's nice to have a format in mind,
at least to get you started.
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Another thing to mention is that unlike
a scientific article, at the end,
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you might want to include
a recommended solution.
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If you think you've found a solution
to the problem, make sure to share it.
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It really helps bring things full circle.
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Also, before we go,
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you should know that we haven't found
anything particularly alarming here.
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The Boston Marathon uses different
qualifying times for different age groups.
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So it's sort of an open secret that
you'll have an easier time qualifying
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as a 35 year old instead of a 34 year old.
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So maybe our recommendation would
be to get rid of age groups and,
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instead, have a different
qualifying time for each age.
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There's so
much you can do with data analysis,
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from figuring out which peanut butter to
buy to finding a good deal on a house,
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data analysis informs our every decision.
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But this is just the beginning, there's
lots more to learn about data analysis.
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Until next time.
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