Making Inferences from Data: Introduction

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En provenance du cours de Johns Hopkins University

Managing Data Analysis

1740 notes

Johns Hopkins University

Managing Data Analysis

1740 notes

Cours 3 sur 5 dans Specialization Executive Data Science

This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling, interpretation, and communication. In addition, we will describe how to direct analytic activities within a team and to drive the data analysis process towards coherent and useful results. This is a focused course designed to rapidly get you up to speed on the process of data analysis and how it can be managed. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know how to…. 1. Describe the basic data analysis iteration 2. Identify different types of questions and translate them to specific datasets 3. Describe different types of data pulls 4. Explore datasets to determine if data are appropriate for a given question 5. Direct model building efforts in common data analyses 6. Interpret the results from common data analyses 7. Integrate statistical findings to form coherent data analysis presentations Commitment: 1 week of study, 4-6 hours Course cover image by fdecomite. Creative Commons BY https://flic.kr/p/4HjmvD

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Managing Data Analysis

Welcome to Managing Data Analysis! This course is one module, intended to be taken in one week. The course works best if you follow along with the material in the order it is presented. Each lecture consists of videos and reading materials that expand on the lecture. I'm excited to have you in the class and look forward to your contributions to the learning community. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and I hope you enjoy the course!

Making Inferences from Data: Introduction5:08

Populations Come in Many Forms4:23

Inference: What Can Go Wrong7:23

Rencontrer les enseignants

Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

Inference is the task of making statements about things you can't observe.

This lecture's about the ingredients you need to make useful inferences from data.

So the goal of inference is to be able to make statements about things we

can't really observe directly.

And we also want to be able to characterize our uncertainty about

those statements that we make.

So these things that we can't observe, the things that we wanna make inferences

about, these are usually referred to generally as the population.

Okay, and then to make inference,

what we do is we collect a sample from the population.

And the sampling process that we use results in our dataset, okay.

So we have a dataset that results from a sampling process that

draws from a population.

So to make inferences from data, you need three simple ingredients.

First, you need to be able to identify the population

to which you're making these inferences.

Of course you have to have a question about that population, but

that's kind of secondary.

Once you've identified the population,

then you can talk coherently about what you're making inference about.

The next thing you need to be able to do is to describe the sampling process.

You need to be able to tell a story about how the data came from the population and

ended up in your lap.

Okay? If you can't describe that process,

then it is going to be very hard to understand how

valid your inferences are from the data.

The better and more clearly you can describe how the data came to you,

the stronger your results will be.

Now the last thing you need is a model for the population.

You need a model for how either elements of the population interact with each other

or how they're related to each other.

So defining the population is far and away the most important task.

If you can't coherently describe the population,

then you can't really make any inferences.

Okay because there's nothing to make inference to.

So make sure that you can accurately characterize your population.

It's gonna help you tell the story better in the end after you analyze the data.

So similarly it's important that you identify an important question that you

wanna ask.

Just get an answer.

But presumably you've done this already.

[COUGH] So just as a very quick,

simple running example, here's a basic population of ten penguins.

Okay, and each of these penguins has a turquoise hat or a purple hat.

And so the basic question that we wanna ask is,

what proportion of this population of penguins is wearing a turquoise hat?

So, that's basically it.

Now, the problem, of course,

is that we can't collect data on the population, right?

Cuz they're penguins after all, and we can't take care of all of them, so

we need to draw a sample from this population.

So the sampling process is the manner in which the data come to you, and

this gives you the dataset.

So, let's say we're going to sample three penguins,

and the way that we sample those three penguins is, we stand there in

front of these ten penguins and we take the first three that walk up to us, okay?

So then we have our dataset of just three penguins.

And so, now the model for the population describes how the features of

the units in the population are related to each other, okay?

And this model can be more or

less complex depending on the type of question that we're trying to answer.

But the important thing is that the model connects the data that we observe

to the population that we don't observe.

All right?

And so, it's basically a little cartoon of how the world works.

So a couple of example of what a model might consist of is that we might assume,

for example, that the units of the population are independent of each other.

So imagine that the different penguins in this population

are wearing different hats.

And the fact that one penguin is wearing a turquoise hat doesn't really influence

whether another penguin is gonna wear a purple hat or a turquoise hat.

So they're all kind of independent of each other.

That's one assumption that we'll make as part of our model.

Another intentional assumption that we might want to make is that

certain features of the population are linearly related.

So there are linear relationships between them.

And there are many other kinds of things that you might wanna assume about

a population if your question's a little bit more complex.

Now the question that we're asking here is very simple.

We just wanna know what's the proportion of penguins with turquoise hats.

So we don't have to make too many assumptions about the population in order

to order that question.

So given our little data set here of three penguins,

we want to estimate what's the proportion with turquoise hats in the population.

So from the dataset we can estimate that it's one-third of the dataset.

One-third of the penguins have turquoise hats so

we'll assume that one-third of the population wears turquoise hats.

So that's what we're gonna infer from our dataset.

Our final estimate is one-third or .33 penguins with turquoise hats.

Now it turns out because we know the population that the truth is about

point four penguins actually wear turquoise hats, or two-fifths, okay?

So our estimate is 0.3 roughly and the truth is point four.

And so the question is well how do we know if 0.3 or 0.33 is a good estimate?

So we'll talk about that in the next lecture.

In terms of how do you know whether you've made a good inference or not.

But the point is that our estimate is .33 and the truth is .4.

So there's a variation there as we would expect.

But that's our estimate for the population level feature.

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