Stat 100

[BarTopDancer]

Halp - Margin of Error

Question:

A question on a youth survey was “How well do you get along with your parents?” The survey showed that 54% of the randomly selected teenagers said they get along “very well.” The reported margin of error was 5%. Is it reasonable to infer that the majority of teenagers get along “very well” with their parents? Provide reasoning.

My Answer:

According to the table in the syllabus, a 5% margin of error would mean 400 teens were surveyed. A 5% margin of error also brings down the sample percent to 49% of teens that say they get along very well with their parents. This means the majority of teens do not say they get along “very well” with their parents. However, if a larger sample was used and the margin of error shrank it is highly probable that the percentage would show that the majority of teens say they do get along very well with their parents.

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Am I on the right track or over-thinking it again?

[BarTopDancer]

Had to turn it in. No time needed for that one.

Test this week. Scored an 80% on the practice test (it was only 5 questions) so hopefully I do ok on the actual test.

[Kevy Baby]

Originally Posted by BarTopDancer

Test this week. Scored an 80% on the practice test (it was only 5 questions) so hopefully I do ok on the actual test.

Hopefully you get more than four correct on the actual test (which presumably is longer than 5 questions)!

[BarTopDancer]

That's the idea.

[BarTopDancer]

Can someone explain p-values to me? I don't get it

[Kevy Baby]

Originally Posted by BarTopDancer

Can someone explain p-values to me? I don't get it

It is the measurement in liquid volume of the amount of urination.

[BarTopDancer]

I knew I shoulda put a disclaimer.

Seriously. Can someone explain them to me? I don't get them and they will be on the test. I don't have to know how to calculate, just interpret them.

[Alex]

I don't know how much detail you want (and to go very far I'll have to look some things up). And it would probably be better to have you try to explain it and then lead you through it but that won't work well over a message board.

Hopefully this helps, but if nothing else I suppose it gives a starting point for specific questions.




Essentially p-value is one part of a tool for determining whether a specific statistical outcome's is so far from what you'd expect to happen by random chance that it should be viewed as statistically significant.

The classic example is in flipping a coin. If it is a perfectly fair coin and you flip it 100 times, statistically you would expect to get 50 heads and 50 tails.

However, when you really perform the experiment, you end up with 57 heads and 43 tails. And statistically every possible combination from 0 heads/100 tails to 100 heads/0 tails is possible. But they are not all equally likely. Calculating the p-value is calculating how likely that result was.

The p-value here would be the specific odds of getting a result deviating 7 values from the expected (so the p-value would be the odds of getting either 57 heads or 57 tails). I'm not going to do the math -- and it can be complex so I am assuming you won't need to either -- but let's say this results in a p-value of .0713 or 7.13%.

This would mean that 7.13% of the time, when flipping a coin 100 times you'd expect to get a result of 57 (in either direction).

Now, p-value is not used by itself. It is just a statement of fact, not of importance. So it is used to help decide if a result is significant. For example, when doing a medical study, if 60% of those taking a drug are cured in 3 days, is this significant when those taking the placebo see a 55% cure rate over 3 days or was it just random deviation?

So, before doing your experiment/measurements you need to decide what your significance level will be. A very common one is 5%. This means that it was decided (and it is an arbitrary decision though different fields have professional standards) that an outcome will be viewed as statistically significant only if the observed outcome had less than a 5% chance of happening purely by chance (the p-value).

In our example, the p-value of 57 heads was 7.13%. So if we were using a 5% standard, getting 57 heads would not be viewed as statistically significant. Therefore it can not be viewed as providing support for a hypothesis that the coin is not fair. If however, you got 61 heads, the p-value for that is (again, just making up a number) 2.67%.

2.67% is less than the significance level of 5% which means it would be viewed as statistically significant. One important thing to keep in mind is that this does not prove anything, it is just evidence in favor of the hypothesis that the coin is not fair.

[Kevy Baby]

I assume you've tried the usual suspects for knowledge?

Wikipedia
One site
Another site

[BarTopDancer]

Thank you Alex!!!!!!!! That does help. I'll post some more soon. The assignment is done, I missed the questions I thought I nailed. Teacher tried to explain it but it didn't make any more sense then the lesson did. I really appreciate you taking the time to help.

Kevy, no I did not check those sites. I have a book and a ton of online resources and just don't "get it". I've had pretty good luck with Alex, Moonie and GD helpin and I am so grateful that they are able to.


just need a D. just need a D. just need a D.