Question

Which of the following are principles of experimental design?
A. biasing
B. replication
C. matching
D. all of the above

Answer 1

@CliffSedge no idea....

Answer 2

maybe B?

Answer 3

Explain why you think it is B.

Answer 4

Well when you experiment you're trying to conclude some data or prove some theory?

Answer 5

so replication? maybe not....

Answer 6

What is "matching" in this context?

Answer 7

This site is rather good: http://stattrek.com/experiments/what-is-an-experiment.aspx?tutorial=ap

I've used it for years. Very straight-forward and clear.

Answer 8

Cannot find an answer....

Answer 9

About halfway down the page, under "Characteristics of a Well-Designed Experiment"

Answer 10

yeah replication I win baby!

Answer 11

The next part says

Two variables in a study are said to be confounded if
A. one cannot separate their effects on a response variable.
B. they are highly correlated.
C. they do not have a normal distribution.
D. one of them is a placebo.

But I'm looking at Confounded and I'm not to sure hmm...

Answer 12

Maybe A?

Answer 13

A looks good. It's very similar to B though... may want to double-check the definitions.

Answer 14

I thought it was B, however I think they are different...

Answer 15

yeah, they are definitely different.

Confounding variable is like a hidden variable.

You expect input A to produce response B. But in reality, input A and also hidden input C produce response B. You mistakenly attribute the response B to A when in reality, some portion of the response is due to the hidden input or confounding variable C.

Answer 16

I think an example of correlation might be a study of basketball players... is shooting skill or height the primary input variable affecting points scored? It is likely these variables are correlated... being super tall gives you a huge (ha!) edge in shooting, increasing your shooting percentage as well as your points scored. But which input variable is responsible for determining output of points scored? You can't tell, because the two inputs are correlated.

Answer 17

@jim_thompson5910 thoughts?

Answer 18

Jake so what do you think?

Answer 19

I guess I would go with A... you can't separate the effects. My correlation example was phrased in these same terms, buts ince you know it's a confounded case, and becauses I don't think confounded is the same as correlated, then I think it means that confounded variables cause effects that can't be separated... for instance, if one is hidden from the experimenter's observation, then all effects are lumped together as coming from a single input, and there is no way to separate.

I'm not 100% sure, but I'm reasonably sure.

Answer 20

Yeah.. C and D are out, but B could be one way or another, you don't know... It seems like things don't have to.. But now that I look at A I'm not too sure what they are even trying to say. At first I thought it was saying that they could be different and their resultrs don't interfear with each otrher.

Answer 21

Confounding
Confounding occurs when the experimental controls do not allow the experimenter to reasonably eliminate plausible alternative explanations for an observed relationship between independent and dependent variables.

Consider this example. A drug manufacturer tests a new cold medicine with 200 participants - 100 men and 100 women. The men receive the drug, and the women do not. At the end of the test period, the men report fewer colds.

This experiment implements no controls! As a result, many variables are confounded, and it is impossible to say whether the drug was effective. For example, gender is confounded with drug use. Perhaps, men are less vulnerable to the particular cold virus circulating during the experiment, and the new medicine had no effect at all. Or perhaps the men experienced a placebo effect.

This experiment could be strengthened with a few controls. Women and men could be randomly assigned to treatments. One treatment group could receive a placebo, with blinding. Then, if the treatment group (i.e., the group getting the medicine) had sufficiently fewer colds than the control group, it would be reasonable to conclude that the medicine was effective in preventing colds.

Answer 22

That last post seems to me to confirm that it is answer A...

Answer 23

Thanks :). That's what relaly made sense to me too.