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Normal Approximation to Binomial

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Normal Approximation to Binomial

<p>Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be <b>no labs</b> for this week. Please don't hesitate to post any questions, discussions and related topics on <a href="https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this week's forum</b></a>.</p>

Introduction to Probability and Data

課程 1（共 5 門，Statistics with R 專項課程）

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

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Statistics, R Programming, Rstudio, Exploratory Data Analysis

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Mine Çetinkaya-Rundel
Associate Professor of the Practice

腳本

In this video, we will discuss shapes of binomial

distributions, and take a look at how they change

as we tweak some of its paramaters, such as

the number of trials or the probability of success.

We will also talk about the fact that when the number of trials increases, the

shape of the binomial actually starts looking

closer and closer to a full normal distribution.

And for such situations we're going to use methods we've learned to

calculate normal probabilities to approximate binomial probabilities.

Say we have a binomial random variable with probability of success 0.25.

This is what the distribution looks like when n is equal to 10.

Let's pause for a moment and carefully examine what we're seeing here.

Each bar represents a potential outcome.

With ten trials, the number of successes could

range anywhere from 0 to 10 and therefore

we have 11 bars here.

Heights of the bars represent the likelihood of these outcomes.

For example, the probability of zero successes can be calculated as 0.75.

The probability of failure raised to the 10th

power, since zero successes basically means ten failures.

This value comes out to be approximately 0.056, which is the height of this bar.

With n equals 10 and p equals 0.25, the expected number of successes is 2.5.

And hence the distribution is centered around this value.

So, the binomial distribution, with p equals

0.25 and n equals 10 is right skewed.

Let's increase the sample size a bit keeping p constant at 0.25.

With n equals

20 we see a change in the center of the

distribution, which is expected since n times p is now different.

But we also see a change in the shape.

The distribution, while still right-skewed, is looking much less skewed.

Increasing the sample size further to 50, the distribution looks even more

symmetric, and much smoother, and increasing

the sample size even further to 100,

the distribution looks no different than the normal distribution.

So let's take a look at why this might be of

interest, within the context of data from a study on Facebook usage.

A recent study found that Facebook users get more than they give.

For example, 40 percent of Facebook users in our

sample made a friend request, but 63 percent received at

least one request.

Users in the sample pressed the like button next to friends' content an

average of 14 times, but had their content liked an average of 20 times.

Users sent nine personal message on average but received 12.

12% of users tagged their friend in a

photo, but 35% were themselves tagged in a photo.

So what explains this phenomenon?

The answer is power users.

Those who contribute much more content than the typical user.

I'm sure you all have a few friends like that, who

are so much more active than everyone else on your friend list.

Some of the other findings from the study are

that 25% of Facebook users are considered power users.

So these are the ones that give more than they get.

And that the average

Facebook user has 245 friends.

We're looking for the probability that an average Facebook user with

245 friends have 70 or more friends who are power users.

So what do we have here?

25% are considered power users, which means that probability of

success is 0.25. And the average Facebook user has 245

friends, meaning that n is equal to 245.

The probability we're interested in is 70 or more power user friends,

which translates to number of successes equal to or greater than 70.

We have n equals 245 trials, a fixed number.

Each trial outcome can be classified as a success or a failure, power user or

not power user.

The probability of success is the same for each trial, 25%.

And we're going to assume that the trials are independent.

They might not be in reality, since if you're the type of person to have some

friends who are power users, the others might

be more likely to be power users as well.

But again, we're going to assume independence for the sake of this example.

This is what the binomial distribution

with n is equal to 245, and p is equal to 0.25 looks like.

And we're interested in the probability of 70 or more

successes, meaning that 70 or more power-user friends among 245.

What does mean?

That's 70, or 71, or 72 all the way up to 245.

So what we're interested in is the sum of probabilities

of each one of these outcomes 70 through 245.

We can calculate each one of these probabilities using the binomial formula

and add them up, but that really does not sound like fun.

This is where the resemblance between the binomial

distribution and the normal distribution comes in very handy.

The blue-shaded area of interest can just as

well be calculated as the area under the smooth

normal curve that closely resembles the more jagged binomial distribution.

Because calculating a shaded area under the normal

curve is a much simpler task than calculating individual

binomial probabilities for all of these outcomes and

adding them up, we might want to use that method.

To calculate a normal probability, we need a little

more information on the parameters of the normal distribution.

These can be estimated by the mean and the standard deviation of the original

binomial distribution. The mean is n times p, so that's 245

times 0.25, 61.25, and the standard deviation

is the square root of 245 times 0.25 times 0.75

Which comes out to be 6.78. So among 245 friends,

we expect 61.25 power users, give or take 6.78.

Given an observation, the mean, and the standard deviation, we

can calculate the area under the curve via a z score.

So the z score is going to be the observation 70 minus 61.25,

the mean, divided by 6.78, the standard deviation, which comes

out to be 1.29.

We can then find the probability of a z score being greater than 1.29, since

we shaded the area underneath the curve beyond the observation of interest.

So we want to take a look on our table to 1.29 as a z score, and in the

intersection of the row and the column of interest, we can see 0.9015.

The probability of obtaining

a z score greater than 1.29 is going to be one minus that probability from the table.

Why are we doing this one minus bit?

Well, because the table always gives us the percentile or the area under the

curve below the observed value and we want to find the complement of that.

Which comes out to be 0.0985. So there is a 9.85%

chance that an average Facebook user, with 245 friends,

has at least 70 friends who are considered power users.

We can also directly calculate this probability using

R and the D binom function we've seen before.

The first argument in the function is the number of

successes, and we're interested in everything between 70 and 245.

The second argument is the total sample size, 245, and

the third is a probability of success for each trial.

So what this function here is doing is actually two things.

First, calculating the probabilities for each outcome 70,

71, 72, all the way up to 245,

and then we wrap that around with the sum function, so we're adding all of that up.

And the probability comes out to be 0.113, or 11.3%.

Versus the 0.0985 we found before.

Why are these values ever so slightly different?

On one hand, it makes sense.

We called the approach the normal approximation to the binomial after

all, so it's just an approximation and not an exact result.

On the other hand, if we need

the exact probability, the difference may be frustrating.

Let's take a closer look at the

binomial distribution and the normal approximation to it.

We can see that the red normal curve is

slightly different than the bars

representing the exact binomial probabilities.

It falls a little bit short.

Also, under the continuous normal distribution, the probability

of exactly 70 successes is undefined. So the shaded

area above 70 doesn't exactly include the

probability of 70 successes. A common fix to this

problem is a 0.5 adjustment to the observation of interest.

So we calculate the z score using 69.5 as opposed to 70, which yields

an adjusted z score of 1.22.

Everything else about the method stays the same.

And the result we get, and you can confirm this using a table or a

computation, is now much closer to the exact

result from the binomial distribution, 0.1112 versus 0.113.

One other method for calculating binomial probabilities is using an applet.

So let's

go to this website where the applet can be found and

let's take a look to see how we can calculate this probability.

We're working with a binomial distribution so

that's the distribution that we're going to pick.

Our number of trials or number of prints here is 245.

So we're going to slide n across to 245,

and our probability of success is 0.25, so we're

going to slide the p to 0.25.

We're looking for the area above 70, so let's take our cutoff value to 70.

And remember that we're looking for the upper tail.

And we're looking for greater than or equal to.

So we want to pick our bound to be that as well, and

once again we can see that same probability, 11.3% chance of having

70 or more power user friends among a sample of 245 friends.

In the example we just presented, we

plotted the binomial distribution using computation, and

visually confirmed that it looked unimodal and

symmetric, roughly similar to a normal distribution.

But what if we couldn't plot the binomial distribution?

What are some guidelines that we can use to determine whether the sample size or

the number of trials is large enough, such that we can be confident in estimating

the binomial distribution using the normal?

In other words, how can we tell if the shape of the binomial

distribution is going to be unimodal and

symmetric, and closely follow the normal distribution?

The rule of thumb is the success-failure condition.

Which says that a binomial distribution with at least 10 expected

successes and 10 expected failures closely follows a normal distribution.

So that's n times p needs to be greater than or equal to ten,

and, n times 1 minus p needs to be greater than or equal to 10.

And in cases where it does we can

approximate the binomial distribution with the normal, where

the parameters of the normal distribution are calculated

as the mean and standard deviation of the binomial.

We also talked about the 0.5 adjustment to make the probabilities calculated

using the normal approximation much closer to

the exact probabilities from the binomial distribution.

But I encourage you to not focus on those details a

whole lot, but instead try to focus on the bigger picture.

Remember that the binomial distribution with sufficient

sample size starts to look nearly normal.

This is important and we're emphasizing this here

because when we later on get to doing inference

for categorical variables with two outcomes, so those are

kind of like Bernoulli outcomes that follow a binomial distribution.

We're going to make use of the fact that

the distributions start to look sl, nearly normal, and

we're going to apply methods that are based on

the normal distribution to do inference for these variables.

Let's do a quick practice problem.

What is the minimum n, or the sample size, required for

a binomial distribution with probability of success

equaling 0.25, to closely follow a normal distribution?

We know that n times p needs to be greater than or equal to ten, and

n times one minus p needs to be greater than or equal to ten as well.

So for both of these equations we want to solve for n and then we're

going to take the maximum of those since that's going to be the minimum required

sample size.

Well, for n times 0.25 to be greater than or equal

to ten, n needs to be greater than or equal to forty.

For n times 0.75 to be greater than or equal to

ten, n needs to be greater than or equal to 13.33.

So the answer is, we need at least forty observations for a binomial distribution

with p equals 0.25, to closely follow a normal distribution.

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