
タイトル：
Independent Samples  Intro to Inferential Statistics

概説：

Welcome to Lesson 11. In Lesson 10, you learned about dependent samples or

repeated measures. Just to refresh your memory, that could be where we give the

same person two different conditions to see how they react to each one. Maybe a

control and then a treatment, or maybe two types of treatments. Or, this could

be longitudinal, where we measure some variable at some point in time, and then

again at another and see if the variable changes. Or this could be a pretest

and posttest. What was the measurement of a variable before and after a

treatment? These are just a few situations in which we would use dependent

samples. This type of research design is really useful because it controls for

indivdual differences. In other words, if we gave someone some kind of

treatment, then those same individual differences will be present the next time

we give that same treatment. That way we can see how two different treatments

play out under the same conditions. Because there's controls for individual

differeneces, we could then use fewer subjects. And this is more cost

effective, less time consuming, and generally less expensive. However, there

are also a few disadvantages. One of which, is carryover effects. For example,

let's say we have this new method of teaching math. You want to know if it's

going to be effective. If you use the same group of students to test this new

teaching method, inevitably, they're going to be better at math the second time

around. So, if the first time we teach them one way, and then the second lesson

we teach them a different way, they'll already be better at math from learning

it the first time. Then we don't know if the results after the second treatment

are due to the fact that it was effective or due to the fact that they've

learned math before. That's just one example. The second measurement can be

affected by the first treatment. And the order in which we give the treatment

might influence the results. For example, say we want to test two types of

pills. What if the first pill has some kind of interaction with the second

pill? And so, by taking it in that order they affect the results. Therefore, in

this lesson, you're going to learn about independent samples. Whereas,

dependent samples deals with within subject designs, independent samples deals

with between subject designs. In this case, the advantages of dependent samples

are the disadvantages of independent samples. And the disadvantages of

dependent samples are the advantages of independent samples. Does that make

sense? With independent samples, we need more subjects because we need to

randomize the two groups taking the two treatments. We need a larger end to

control for individual differences as best as possible. That means it's more

time consuming and generally more expensive. But then the advantages of

independent samples are that we don't have carry over effects. Therefore, we

can give one treatment to one group, another treatment to another group, and

not worry about one treatment effecting the other, because each person or each

subject only gets one treatment. With independent samples, we can do an

experimental test where we give treatments to the subjects. Or observational,

where we simply observe characteristics of two different populations, and then

compare them. Everything is exactly the same. The Null and Alternative

Hypotheses, the tstatistic, and the way we make our statistical decision.