6.01 Statistical inference

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来自 University of Amsterdam 的课程

统计基础

1988 个评分

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University of Amsterdam

统计基础

1988 个评分

课程 3（共 5 门，Specialization 社会科学的方法和统计）

Understanding statistics is essential to understand research in the social and behavioral sciences. In this course you will learn the basics of statistics; not just how to calculate them, but also how to evaluate them. This course will also prepare you for the next course in the specialization - the course Inferential Statistics. In the first part of the course we will discuss methods of descriptive statistics. You will learn what cases and variables are and how you can compute measures of central tendency (mean, median and mode) and dispersion (standard deviation and variance). Next, we discuss how to assess relationships between variables, and we introduce the concepts correlation and regression. The second part of the course is concerned with the basics of probability: calculating probabilities, probability distributions and sampling distributions. You need to know about these things in order to understand how inferential statistics work. The third part of the course consists of an introduction to methods of inferential statistics - methods that help us decide whether the patterns we see in our data are strong enough to draw conclusions about the underlying population we are interested in. We will discuss confidence intervals and significance tests. You will not only learn about all these statistical concepts, you will also be trained to calculate and generate these statistics yourself using freely available statistical software.

从本节课中

Confidence Intervals

We can distinguish two types of statistical inference methods. We can: (1) estimate population parameters; and (2) test hypotheses about these parameters. In this module we’ll talk about the first type of inferential statistics: estimation by means of a confidence interval. A confidence interval is a range of numbers, which, most likely, contains the actual population value. The probability that the interval actually contains the population value is what we call the confidence level. In this module we’ll show you how you can construct confidence intervals for means and proportions and how you should interpret them. We’ll also pay attention to how you can decide how large your sample size should be.

6.01 Statistical inference3:56

6.02 CI for mean with known population sd5:44

6.03 CI for mean with unknown population sd7:37

与讲师见面

Matthijs Rooduijn
Dr.
Department of Political Science
Emiel van Loon
Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics

Uhm

In Dutch: Welkom allemaal bij deze video over betrouwbaarheid.

>> [BLEEP] Yeah, well,

sorry, I'm a little confused.

The thing is me and my girlfriend got a baby a couple of weeks ago and well,

I don't sleep that much anymore.

And because I'm a little tired, or a better word might be exhausted,

it could be the case that I'm a little less focused then I usually am.

But let me be a little more concrete about what's going on.

I usually slept about eight hours every night

now I only sleep about five hours per night.

That means a reduction of three hours per night.

That's about 20 hours per week and about 1000 hours per year.

That's about 40 days.

In other words, if my daughter continues with her present sleeping schedule,

after a year I will have lost about 40 full days of sleep, just to let you know.

But sleep or no sleep, I'm still a scientist and

I was wondering how much sleep other new parents in my home city of Amsterdam lost

after they had their first baby.

Are me and my girlfriend the only ones losing so much sleep?

Suppose I contacted the local authorities and

asked them the contact details of all new parents in Amsterdam.

Let's say that new parents are those who got a baby within the last six months.

I drew a simple random sample of 60 new parents and

asked them how much hours per night they slept less than before they had a baby.

In this module, I will talk about statistical inference.

I will, on the basis of sample information, draw conclusions about

the entire population from which the sample was drawn.

We can distinguish two types of statistical inference methods.

We can estimate population parameters, and

we can test hypotheses about these parameters.

In this module, I will talk about the first type of inferential statistics.

The second type, hypothesis testing, will be discussed in the next module.

There are two ways in which we can estimate the value of a population

parameter.

The first one is a so-called point estimate.

It is a single number that is our best guess for the population parameter.

The second one is the interval estimate.

It is a range of values within which we expect the parameter to fall.

Lets assume that the mean number of hours that the 60 respondents in my

sample slept less after they had their first baby is 2.6.

That means that a good point estimate for

the mean number of lost sleeping hours in the population is, well, 2.6.

In other words, the statistic x-bar, which in our case is 2.6 hours,

is a good point estimate for the parameter mu.

However, one individual point estimate doesn't tell us if the estimate

is close to the population parameter we're interested in or not.

Therefore, next to a point estimate,

researchers often also want to know the likely precision of the point estimate.

They show this likely precision by also computing an interval estimate.

An interval estimate is a range of numbers which,

most likely, contains the actual population value.

On the basis of our sample mean of 2.6 hours, we might predict, for

instance, that the mean lost sleeping hours of all new parents in Amsterdam

lies somewhere between 2.3 and 2.9.

The probability that interval contains the population value is what we call

the confidence level.

The confidence level always has a value close to 1, in most cases it's 0.95.

In that case, we'll talk about a 95% confidence interval.

In the next videos I'll discuss how we can construct such confidence intervals.

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