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• All right!

• Before crunching any numbers and making decisions, we should introduce some key definitions.

• The first step of every statistical analysis you will perform is to determine whether the

• data you are dealing with is a population or a sample.

• A population is the collection of all items of interest to our study and is usually denoted

• with an uppercase N. The numbers weve obtained when using a population are called parameters.

• A sample is a subset of the population and is denoted with a lowercase n, and the numbers

• weve obtained when working with a sample are called statistics.

• Now you know why the field we are studying is called statistics ?

• Let’s say we want to make a survey of the job prospects of the students studying in

• the New York University.

• What is the population?

• You can simply walk into New York University and find every student, right?

• Well, probably, that would not be the population of NYU students.

• The population of interest includes not only the students on campus but also the ones at

• home, on exchange, abroad, distance education students, part-time students, even the ones

• who enrolled but are still at high school.

• Though exhaustive, even this list misses someone.

• Point taken.

• Populations are hard to define and hard to observe in real life.

• A sample, however, is much easier to contact.

• It is less time consuming and less costly.

• Time and resources are the main reasons we prefer drawing samples, compared to analyzing

• an entire population.

• So, let’s draw a sample then.

• As we first wanted to do, we can just go to the NYU campus.

• Next, let’s enter the canteen, because we know it will be full of people.

• We can then interview 50 of them.

• Cool!

• This is a sample.

• Good job!

• But what are the chances these 50 people provide us answers that are a true representation

• of the whole university?

• Pretty slim, right.

• The sample is neither random nor representative.

• A random sample is collected when each member of the sample is chosen from the population

• strictly by chance.

• We must ensure each member is equally likely to be chosen.

• Let’s go back to our example.

• We walked into the university canteen and violated both conditions.

• People were not chosen by chance; they were a group of NYU students who were there for

• lunch.

• Most members did not even get the chance to be chosen, as they were not on campus.

• Thus, we conclude the sample was not random.

• What about representativeness of the sample?

• A representative sample is a subset of the population that accurately reflects the members

• of the entire population.

• Our sample was not random, but was it representative?

• Well, it represented a group of people, but definitely not all students in the university.

• To be exact, it represented the people who have lunch at the university canteen.

• Had our survey been about job prospects of NYU students who eat in the university canteen,

• we would have done well.

• By now, you must be wondering how to draw a sample that is both random and representative.

• Well, the safest way would be to get access to the student database and contact individuals

• in a random manner.

• However, such surveys are almost impossible to conduct without assistance from the university!

• We said populations are hard to define and observe.

• Then, we saw that sampling is difficult.

• But samples have two big advantages.

• First, after you have experience, it is not that hard to recognize if a sample is representative.

• And, second, statistical tests are designed to work with incomplete data; thus, making

• a small mistake while sampling is not always a problem.

• Don’t worry; after completing this course, samples and populations will be a piece of

• cake for you!

• Keep up the good work and thanks for watching!

All right!

B1 中級

# 人口與樣本 (Population vs sample)

• 1 0
林宜悉 發佈於 2021 年 01 月 14 日