Transcriber: Emma Gon Reviewer: Anna Sobota
It's not hard to notice that the world is quite polarized these days.
Do you wholeheartedly support the current president or prime minister
or do you despise that person?
Do you always wear a face mask in every possible circumstance?
Or do you refuse to ever put one on?
Is the world getting hotter?
Where did COVID come from?
Do we need another oil pipeline crisscrossing the countryside?
I suspect you feel strongly and are very certain about your answers
to some, if not all, of these questions.
15 years ago, I was certain about at least one thing.
I really, really hated statistics.
Oh, it was the worst.
I was a math major and mathematics is so beautiful.
When you prove a theorem, when you solve an equation,
it’s 100 % true.
QED, we’re done.
It’s a brick on the wall that can never be removed.
In contrast, statistics is a mess.
Hypotheses get proposed.
Hypotheses get rejected.
Sometimes you get a false positive.
Sometimes you get a false negative.
Didn’t like it.
I didn’t like it,
that is until I got a job working in national security
for the U.S. Department of Defense.
Then I realized that the world, the real world,
is messy and to make sense of all this chaos, we need statistics.
Now, statisticians try to answer questions just like mathematicians do.
But while a mathematician would craft a careful, logical,
flawless argument in order to prove a theorem,
a statistician collects data in order to test a hypothesis.
Sir Ronald Fisher, one of the founding fathers of statistics,
once said that if we’re at least 95 % certain that a hypothesis is correct,
then yes, we can claim that it's true.
But that would imply that there’s a one in 20 chance that we’re wrong.
If you’re 95% certain that a new economic policy
would decrease homelessness here, in the city,
then, yes, that’s worth a shot.
If you’re only 95 % sure that that new bridge to downtown
will remain standing one year from now,
I am not crossing that thing.
To address the ever increasing polarization in our society,
I claim we should act more like statisticians.
Now, that does not mean we should jump on the AI data science bandwagon?
No.
I mean, we should act philosophically, like statisticians.
When working in government,
it didn’t matter who was in the White House,
it didn’t matter what they believed,
what mattered was trying to answer questions
tough and challenging questions based on data to whatever extent was possible.
For example, you could say that the evidence suggests
that wearing a mask in an indoor setting
would reduce infection rates by some measurable percent,
plus or minus a margin of error.
That's how I like to think, and that's what I do is a mathematical statistician.
The best way to illustrate statistical thinking is by looking at data,
and there are two data sets that I’d like to talk about now.
The first involves political spin.
It’s early 2020 and the COVID pandemic is spreading across the world.
Politicians for often heard saying,
“Oh, it only appears that we have out of control cases,
and that’s not because of failing policies.
Oh no, no, no, no.
It’s because we’re doing such a good job at testing.
You see other counties and cities and states and provinces,
they appear to have fewer cases than us,
just because they’re not doing a very good job at testing, not like us.”
OK, so that’s political spin.
I mean, that’s textbook political spin.
But is it baseless spin?
To test to this, I looked at two data streams this past winter
with my undergraduate students in time series.
What we looked at were the number of daily COVID cases
reported here, in the province of Alberta
and the number of daily COVID tests performed here, in Alberta.
And when you have two data streams,
you can look at what’s called a cross-correlation.
The cross-correlation is roughly saying,
are these two data streams moving together?
Do they go up and down at the same time roughly?
What we found was that, yes,
there was an extremely strong positive correlation between these two data streams
and it was offset by two days.
And if you recall, when the pandemic first began,
it took about 48 hours to get the results of a COVID-19 test.
So wait, does this mean
that those politicians were right all along?
Well, yes and no.
You see, while there was a very strong positive correlation
between COVID cases reported and COVID tests performed,
the R squared value was only about 20%.
And what that means is about 20 % of the up and down fluctuations
in COVID cases can be attributed to the number of tests performed.
The other 80 % remains unexplained.
It encompasses everything else,
including the spread of the disease, the efficacy of social distancing,
lockdowns, super spreader events.
I mean, even the weather, right, can affect the number of reported cases.
So in the end, whether or not you like the person running your district,
the politicians claim is certainly not 100% right,
but it’s not 100% wrong, either.
Now, in the second data set I’d like to talk about involves mass shootings
in the U.S. between 2017 and 2019.
Now, mass shootings are a big problem there.
They are disturbing weekly feature on news reports,
and there are two vehemently opposing solutions ideas
on how to solve this problem.
The left says, “We need fewer guns.”
See, fewer guns would mean fewer mass shootings, fewer deaths.
OK, there’s a logic behind that, could be true.
The right says, “No, we need more guns.”
You see, gun laws will not stop a mass shooter,
but a bystander with a gun can stop a mass shooter before people are killed.
And at least anecdotally, that has happened before.
There are cases of bystanders shooting shooters,
so that actually may be a solution as well,
but what does the data tell us?
To try to answer this question,
I came up with my own gun strictness score,
a simple zero to six scale
where zero would imply weak gun laws, a state like Texas,
and six would imply strong gun laws, a state like California.
Putting all of this into a statistical model and of course,
adjusting for population, we find that no,
there wasn’t any statistically significant effect on gun laws
on the number of mass shootings.
So wait, does this mean that the right and left are both wrong?
We don't see vigilante citizens taking out mass shooters in any aggregate sense,
but we also don’t see strict gun laws having any real effect either.
This does hint a bit at the mental health argument
so often popularized by right-leaning politicians,
the idea that violent people will commit violence regardless of deterrence.
But that’s not the entire story.
You see,
while the strict gun laws are not decreasing the number of mass shootings,
they are decreasing the number of fatalities.
We can see this, for example, by looking again at Texas and California,
two states that are polar opposites politically,
but the two with the highest population.
And looking at the data and adjusting for population,
we see that California had more mass shootings per capita than Texas.
But Texas had 40 % more fatalities per capita than California.
So are gun laws are going to solve the mass shooting problem?
No, no, they’re not.
But can they save some lives?
And yes, yes they can.
Putting all of this into the statistical model,
we find that going from weak gun laws, like Texas
to stronger gun laws, like California
will decrease the number of fatalities by between 13 % and 66 %.
On the low side, 13 % doesn’t sound like a lot.
But in this case,
13 % is corresponding to people’s lives,
and even a small number can be extremely significant.
So we’ve talked about political spin.
We’ve talked about mass shootings.
Lastly, let's talk about me.
Am I this lofty professor here lecturing you for my outdoor stage,
always right in all of my statistical analyses all the time?
Of course not.
When I look at data, when I talk about it with my students;
now don’t get me wrong,
I spend a lot of effort trying to get it right;
nobody gets it right 100% of the time.
And I encourage you to go out and look at data yourself.
You can find the COVID-19 data I was talking about
through the government of Alberta’s website,
and you can find the mass shooting data via the Gun Violence Archive,
which is a non a non-profit that collects such data.
And you should go out and look at it yourself.
Fit your own models.
Try to come to your own conclusions.
Now I spend my life trying to fit models, statistical models to data.
I understand that not everybody is comfortable with such endeavors.
In that case, you can still look at different reports and different graphics
and plots of the data produced by other people.
And just question how much can we really learn from this data
and how much will inevitably remain unknown?
When going about solving a math problem,
one common strategy is to find a common denominator
to make all the equations work well together.
The next time you see two people yelling at each other on, say, Twitter,
consider that they’re both going to be a little bit right,
and well, they’re both going to be a little bit wrong.
And that’s something that I can confidently say I’m 95% certain about.
Thank you.