## Law of Large Numbers vs the Central Limit Theorem: in GIFs

I’ve spoken about these two fundamentals of asymptotics previously here and here. But sometimes, you need a .gif to really drive the point home. I feel this is one of those times.

Firstly, I simulated a population of 100 000 observations from the random uniform distribution. This population looks nothing like a normal distribution and you can see that below.

Next, I took 500 samples from the data with varying sample sizes. I used n=5, 10, 20, 50, 100 and 500. I calculated the sample mean (x-bar) and the z score for each and I plotted their kernel densities using ggplot in R.

Here’s a .gif of what happens to the z score as the sample size increases: we can see that the distribution is pretty normal looking, even when the sample size is quite low. Notice that the distribution is centred on zero.

Here’s a .gif of what happens to the sample mean as n increases: we can see that the distribution collapses on the population mean (in this case µ=0.5).

For scale, here is a .gif of both frequencies as n gets large sitting on the same set of axes: the activity is quite different.

If you want to try this yourself, the script is here. Feel free to play around with different distributions and sample sizes, see what turns up.

## The Law of Large Numbers: It’s Not the Central Limit Theorem

I’ve spoken about asymptotics before. It’s the lego of the modelling world, in my view. Interesting, hard and you can lose years of your life looking for just the right piece that fits into the model you’re trying to build.

The Law of Large Numbers (LLN) is another simple theorem that’s widely misunderstood. Most often it’s conflated with the central limit theorem (CLT), which deals with the studentised sample mean or z-score. The LLN pertains to the sample mean itself.

Like the CLT, the LLN is actually a collection of theorems, strong and weak. I’ll confine myself to the simplest version here, Khinchine’s weak law of large numbers. It states that for a random, independent and identically distributed sample of n observations from any distribution with a finite mean (µ) and variance: then the sample mean has a probability limit equal to the population mean, µ. That is, the sample mean is a consistent estimator of the population mean under these conditions.

Put simply, as n gets very big, the sample mean is equal to the population mean.

Notice there is nothing about normal distributions as n gets large. That’s the key difference between the LLN and the CLT. One deals with the sample mean alone, the other with the studentised version. On its own, the distribution of the sample mean collapses onto a single point as n gets large: µ. This is the implication of the LLN.

Appropriately scaled, centred and at the correct rate, the studentised sample mean has a normal distribution in the limit as N gets large: that’s the CLT.

As usual, here’s an infographic to go: put side by side the two theorems have different results but are dealing with something quite similar.

## The Central Limit Theorem: Misunderstood

Asymptotics are the building blocks of many models. They’re basically lego: sturdy, functional and capable of allowing the user to exercise great creativity. They also hurt like hell when you don’t know where they are and you step on them accidentally. I’m pushing it on the last, I’ll admit. But I have gotten very sweary over recalcitrant limiting distributions in the past (though I may be in a small group there).

One of the fundamentals of the asymptotic toolkit is the Central Limit Theorem, or CLT for short. If you didn’t study eight semesters of econometrics or statistics, then it’s something you (might have) sat through a single lecture on and walked away with the hot take “more data is better”.

The CLT is actually a collection of theorems, but the basic entry-level version is the Lindberg-Levy CLT. It states that for any sample of n random, independent observations drawn from any distribution with finite mean (μ) and standard deviation (σ), if we calculate the sample mean x-bar then,

In my time both in industry and in teaching, I’ve come across a number of interpretations of this result: many of them very wrong from very smart people. I’ve found it useful to clarify what this result does and does not mean, as well as when it matters.

Not all distributions become normal as n gets large. In fact, most things don’t “tend to normality” as N gets large. Often, they just get really big or really small. Some distributions are asymptotically equivalent to normality, but most “things”- estimators and distributions alike- are not.

The sample mean by itself does not become normal as n gets large. What would happen if you added up a huge series of numbers? You’d get a big number. What would happen if you divided your big number by your huge number? Go on, whack some experimental numbers into your calculator!

Whatever you put into your calculator, it’s not a “normal distribution” you get when you’re done. The sample mean alone does not tend to a normal distribution as N gets large.

The studentised sample mean has a distribution which is normal in the limit. There are some adjustments we need to make before the sample mean has a stable limiting distribution – this is the quantity often known as the z-score. It’s this quantity that tends to normality as n gets large.

How large does n need to be? This theorem works for any distribution with a finite mean and standard deviation, e.g. as long as x comes from a distribution with these features. Generally, statistics texts quote the figure of n=30 as a “rule of thumb”. This works reasonably well for simple estimators and models like the sample mean in a lot of situations.

This isn’t to say, however, that if you have “big data” your problems are gone. You just got a whole different set, I’m sorry. That’s a different post, though.

So that’s a brief run down on the simplest of central limit theorems: it’s not a complex or difficult concept, but it is a subtle one. It’s the building block upon which models such as regression, logistic regression and their known properties have been based.

The infographic below is the same information, but for some reason my students find information in that format easier to digest. When it comes to asymptotic theory, I am disinclined to argue with them: I just try to communicate in whatever way works. On that note, if this post was too complex or boring, here is the CLT presented with bunnies and dragons.** What’s not to love?

** I can’t help myself: The reason why the average bunny weights distribution gets narrower as the sample size gets larger is because this is the sample mean tending towards the true population mean. For a discussion of this behaviour vs the CLT see here.

It’s my only criticism of what was an otherwise a delightful video. Said video being in every way superior to my own version done late one night for a class with my dog assisting and my kid’s drawing book. No bunnies or dragons, but it’s here.

## Data Analysis: Enough with the Questions Already

Ideally, you’d really like to write something that doesn’t leave the reader with a keyboard imprint across their forehead due to analysis-induced narcolepsy. That’s not always easy, but here are some thoughts.

Writing up data analysis shouldn’t be about listing means, standard deviations and some dodgy histograms. Yes, sometimes you need that stuff- but mostly what you need is a compelling narrative. What is the data saying to support your claims?

It doesn’t all need to be there.

You worked out that tricky bit of code and did that really awesome piece of analysis that led you to ask questions and… sorry, no one cares. If it’s not a direct part of your story, it probably needs to be consigned to telling your nerd friends on twitter- at least they’ll understand what you’re talking about. But keep it out of the write up!

How is it relevant?

Data analysis is rarely the end in and of itself. How does your analysis support the rest of your project? Does it offer insight for modelling or forecasting? Does it offer insight for decision making? Make sure your reader knows why it’s worth reading.

Do you have an internal structure?

Data analysis is about translating complex numerical information into text. A clear and concise structure for your analysis makes life much easier for the reader.

If you’re staring at the keyboard wondering if checking every social media account you ever had since high school is a valid procrastination option: try starting with “three important things”. Then maybe add three more. Now you have a few things to say and can build from there.

Who are you writing for?

Academia, business, government, your culture, someone else’s, fellow geeks, students… all of these have different expectations around communication.  All of them are interested in different things. Try not to have a single approach for communicating analysis to different groups. Remember what’s important to you may not be important to your reader.

Those are just a few tips for writing up your analyses. As we’ve said before: it’s not a one-size-fits-all approach. But hopefully you won’t feel compelled to give a list of means, a correlation matrix and four dodgy histograms that fit in the space of a credit card. We can do better than that!

## Data Analysis: More Questions

In our last post on data analysis, we asked a lot of questions. Data analysis isn’t a series of generic questions we can apply to every dataset we encounter, but it can be a helpful way to frame the beginning of your analysis. This post is, simply, some more questions to ask yourself if you’re having trouble getting started.

The terminology I use below (tall, dense and wide) is due to Francis Diebold. You can find his original post here and it’s well worth a read.

## Data Analysis: Questions to Ask the First Time

Data analysis is one of the most under rated, but most important parts of data science/econometrics/statistics/whatever it is you do with data.

It’s not impressive when it’s done right because it’s like being impressed by a door handle: it is something that is both ubiquitous and obvious. But when you’re missing the doorhandles, you can’t open the door.

There are lots of guides to data analysis but fundamentally there is no one-size-fits-most approach that can be guaranteed to work for every data set. Data analysis is a series of open-ended questions to ask yourself.

If you’re new or coming to data science from a background that did not emphasise statistics or econometrics (or story telling with data in general), it can be hard to know which questions to ask.

I put together this guide to offer some insight into the kinds of questions I ask myself when examining my data for the first time. It’s not complete: work through this guide and you won’t have even started the analysis proper. This is just the first time you open your data, after all.

But by uncovering the answers to these questions, you’ll have a more efficient analysis process. You’ll also (hopefully) think of more questions to ask yourself.

Remember, this isn’t all the information you need to uncover: this is just a start! But hopefully it offers you a framework to think about your data the first time you open it. I’ll be back with some ideas for the second time you open your data later.

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