Hi, I’m Adriene Hill, and Welcome back to
Crash Course, Statistics.
Bayesian Hypothesis Testing--or Bayesian Inference--is a great way to model the way we reason about
things in everyday life.
We collect evidence and experience and we
use it to build our beliefs about the world.
We collect information on whether certain
facial expressions mean that someone is upset.
Whether clouds outside mean it’s going to
be cold today.
Or whether people who smoke are more likely to have lung cancer.
But Bayesian methods are useful above and beyond updating our personal beliefs.
Bayes has helped companies make marketing decisions--like which color to use on their website.
And it has helped researchers to quantify
their results in scientific studies.
And today, we’re going to talk about it.
INTRO
First, you may have noticed that so far when we talk about the math of Bayes’ Theorem,
we’ve been using discrete variables, like
whether or not you’re a star wars fan, or
whether or not you have a disease.
But Bayes’ Theorem can help us update beliefs that involve continuous variables too.
The math of Bayes’ Theorem with a continuous variable is a bit more complicated than in
the discrete case.
Science writer Sharon Bertsch McGrayne called it, “a Theorem in want of a computer...”.
In fact for much of the 20th century, Scientists and Statisticians who wanted to use Bayes
were limited in their ability to do so by
a lack of computational power.
But we still want answers to those more complicated problems.
Sometimes we want to know whether dogs who are walked regularly are less likely to damage furniture.
Or whether House Elves have lower intelligence than Wizards...which is an example of Bayesian
Hypothesis testing from a Harry Potter themed article by Alexander Etz and Joachim Vandekerckhove.
Guess you’d better update your prior belief
about how cool statisticians are😎.
The ideas behind continuous and discrete Bayesian Inference are exactly the same.
We take our prior beliefs--what we believe
before we’ve seen new evidence--and update
it with the likelihood of our evidence.
This is called the Bayes Factor when comparing two models.
Once we’ve updated, our new beliefs are
called our “Posterior” beliefs.
If we’re comparing two models, they are
called our posterior odds.
But instead of simple, discrete probabilities, we have probability distributions.
For example, let’s look at the ever present
problem of whether or not a coin is biased.
Before you start your experiment to test the
fairness of your coin, you decide that you
know almost nothing about whether or not it’s biased.
So your prior probability of getting tails
is a uniform distribution between 0 (never
tails) and 1(always tails).
You consider all probabilities of getting
tails--we’ll call that theta--equally likely.
You have a friend flip the coin in question
5 times, and they get 1 tail.
Which seems unlikely, though not impossible for a fair coin.
Using the Binomial Probability Formula we
know that the probability of this happening
with a fair coin is about 16%.
Note this new notation for 5 choose 1, you’re most likely to run into this in the stats world.
So how does this evidence update your belief about what the real probability of getting
a tail is for this coin?
Before we show the Bayesian calculation, let’s take a moment to figure out what we think
without the math.
Since we saw at least one head and one tails, we can rule out both the probabilities 0 and 1.
And we think that probabilities very close
to 0 and 1 are unlikely too.
Because it’d be REALLY rare to see only
one tail if the probability of tails were 0.99.
And similarly rare to see a tail at all if
the probability were 0.001.
Now we can do the Bayesian calculation and see if it matches our intuition.
Here’s Bayes’ Theorem, but for this continuous problem:
We won’t get too stuck on the math, but
we can see that this is the same old Bayes’
Theorem that we’ve seen before...just continuous.
When we plug in this formula to a graphing
program to show our posterior, it looks like this:
The Y axis tells us the relative probability
of a theta--in this case theta is the probability
of getting tails-- and the x axis shows us
all the possible values of theta between 0 and 1.
We can see that we took our prior distribution (the dotted line)...
and updated it using the likelihood of the
data, which told us the probability of getting
1 out of 5 tails for EVERY potential probability of getting tails that a coin could have.
Once we updated our prior beliefs, about which probabilities are the most likely, our posterior
beliefs are represented like this (the solid
line).
Anything on the curve that is above the dotted prior line represents a theta that became
more likely after we saw the data.
And anything on the curve that is below the
dotted line is a theta that became less likely.
And this matches our intuition;
Thetas that are close to 1 and 0 became less
likely, while thetas around 0.1-0.5 became
more likely.
So maybe we have a fair coin here...but it
seems more likely that it’s biased.
Businesses like Bayes because it allows them to take into account previous knowledge and
expert opinion when they make their calculations.
Let’s look at an example of how a business
might use Bayesian inference.
We’ll keep the math to a minimum, but if
you’re interested in learning more, you
can check out this awesome blog post by Will Kurt on countbayesie.com which we based this
next example on.
And the link is in the description.
Say you’re a beauty blogger, and you send
out weekly emails encouraging your followers
to read your latest blog post.
The more people who click, the more money you make, and so you want the most clickable
emails ever.
Your friend, who’s also in the blogging
business, told you that adding a picture at
the top of your email gets more people to
click, but you want to test that idea out
with your own readers.
Normally, your click rate is around 30%, so
you decide to represent your prior beliefs
about your true click rate using this function:
Values around 30% are most likely, but it’s
possible your true click rates are higher
or lower than that.
You randomly select 300 of your followers
to be a part of your experiment--often called
an A/B test in the business world--and send half the email with a cute picture of you
with your poodle, Ginger as well as the normal
content.
The other half gets your standard pictureless email.
You anxiously await the results, anad three
days later you have them:
You use the new information you have about your two emails to update your original beliefs
about your click rate.
Since the two groups were the same before
you assigned them to get either email No Dog
Pictures or with Dog Pictures, you use the
same prior for both groups.
Once you’ve incorporated this new evidence, your Posterior distributions look like this:
And they tell you how likely each click rate
is under your new, posterior beliefs about
each group.
It looks like the group with pictures is likely
to have a higher click rate... but you can’t
know for sure.
One way to get more information to make your decision is to randomly simulate a bunch of
samples - one at a time.
The samples come from each of your two posterior distributions and then you count how often
the group with pictures’ click rate is higher
than the group that didn’t get a picture
in their email.
That percentage will tell you roughly how
likely it is that the group that got pictures
will have a higher click rate than the group
who did not.
You decide that if in 70% of your simulation
samples the group with pictures has a higher
click rate, you’ll include glamor shots
of Ginger in all your new emails.
Using Bayesian methods to analyze this question allowed you to “inject” your own prior
beliefs into the analysis, which is important
when making business decisions.
Businesses often want to make the best decision in the most cost efficient way, which means
taking advantage of all the information you
have; not only data, but prior knowledge of
the field and expert opinion.
Your prior knowledge about the click rate
of your emails made it possible for you to
start your analysis knowing it’s pretty
unlikely your click rate was very near 0,
or very near 1.
Bayesian analyses can be incredibly useful
in science, as well.
A study on Dissociative Identity Disorder
(or DID)--formerly called Multiple Personality
Disorder--looked at whether people with D-I-D had different “memory” between personalities.
If one person had two separate personalities, Bob and Alice, researchers were interested
in whether something that person learned as “Bob” could be remembered by that person
when they were “Alice”.
In order to test this idea, participants were
shown a few pictures and told a story.
They then waited a little while, and answered 15 multiple choice questions about the material.
There were 3 different groups of participants:
A group of DID patients - who were asked to learn the materials in one personality and
switch to another personality before the test.
A pretend amnesiacs group - without DID who did not see the materials.
And a maligners group without DID who saw the materials but were told to pretend they
hadn’t and answer as if they had never heard the story or
seen the pictures.
Researchers wanted to know whether the patients with DID, the people who had never seen the materials,
and  the people who were pretending not to have seen the materials had the same mean accuracy on the test.
This would help researchers and cognitive scientists understand more about how memory works in DID patients.
Using Null Hypothesis Significance testing,
researchers could try to address whether all
three groups had the same mean score on the test, but even if they rejected the null hypothesis
that all three groups are the same, they wouldn’t be able to say how much more likely it was
that all three groups were different.
Bayesian methods can tell you that.
And a Group of researchers did analyze the
data this way, and found out that the Bayes
Factor for these models was about 4,000!
That means that the data that the researchers saw should update our beliefs by a lot.
No matter what you believed before hand, your updated beliefs will most likely reflect the
fact that it’s more likely that these three
groups--DID patients, people who didn’t
see the materials, and people who pretended not to see the materials--are three distinct groups.
And it’s interesting, because it provides
evidence that people with DID may not just
be pretending to not remember things that
were learned while they were in a different
personality... but they may not quite be behaving the same as people who really had never seen
the materials, which is what you might expect if two personalities were completely separate.
And while Bayesian inference is increasingly popular in many scientific fields like Psychology,
it’s also being used right now in many places near you.
Bayesian methods are used to help translate one language to another, and to suggest which
items you might buy next based on the fact that you just bought four silicone sponges,
a Sandalwood Candle, and whiteboard markers.
Bayes can help figure out which allergy medicine you’ll react best to based on your genetic profile.
And Bayes plays a role in creating artificial
intelligence that can do pretty amazing things, like
understanding that it’s more likely that
you said “Siri, Turn on the lights” and
not “Siri, Learn all the Sites !”
Thanks for watching, I’ll see you next time.
