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What assumptions are necessary to make this a valid estimate?

what is a 5 number summary

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With a computed t-statistic of 2.63, what conclusion should you draw?

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Is this homework? What have you tried so far? Did you check the pages for  and n?

The relative costs of false positives and false negatives, and thus the best P value to use, will be different for different experiments. If you are screening a bunch of potential sex-ratio-changing treatments and get a false positive, it wouldn't be a big deal; you'd just run a few more tests on that treatment until you were convinced the initial result was a false positive. The cost of a false negative, however, would be that you would miss out on a tremendously valuable discovery. You might therefore set your significance value to 0.10 or more for your initial tests. On the other hand, once your sex-ratio-changing treatment is undergoing final trials before being sold to farmers, a false positive could be very expensive; you'd want to be very confident that it really worked. Otherwise, if you sell the chicken farmers a sex-ratio treatment that turns out to not really work (it was a false positive), they'll sue the pants off of you. Therefore, you might want to set your significance level to 0.01, or even lower, for your final tests.

On what basis do you offer your consultation to her?

Use a diagram to illustrate what you have done, i.e., draw the reference distributions.

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You should decide whether to use the one-tailed or two-tailed probability before you collect your data, of course. A one-tailed probability is more powerful, in the sense of having a lower chance of false negatives, but you should only use a one-tailed probability if you really, truly have a firm prediction about which direction of deviation you would consider interesting. In the chicken example, you might be tempted to use a one-tailed probability, because you're only looking for treatments that decrease the proportion of worthless male chickens. But if you accidentally found a treatment that produced 87% male chickens, would you really publish the result as "The treatment did not cause a significant decrease in the proportion of male chickens"? I hope not. You'd realize that this unexpected result, even though it wasn't what you and your farmer friends wanted, would be very interesting to other people; by leading to discoveries about the fundamental biology of sex-determination in chickens, in might even help you produce more female chickens someday. Any time a deviation in either direction would be interesting, you should use the two-tailed probability. In addition, people are skeptical of one-tailed probabilities, especially if a one-tailed probability is significant and a two-tailed probability would not be significant (as in our chocolate-eating chicken example). Unless you provide a very convincing explanation, people may think you decided to use the one-tailed probability after you saw that the two-tailed probability wasn't quite significant, which would be cheating. It may be easier to always use two-tailed probabilities. For this handbook, I will always use two-tailed probabilities, unless I make it very clear that only one direction of deviation from the null hypothesis would be interesting.

What would you believe her decision was?

no matter what the population mean is, the probability of deciding wrongly is at most 5%.

Is an report.
I have try researching, but none of the experiment is about flipping multiple coins leading to the shape of a binomial and geometric distribution.
Yes i have read the pages but i think it did not explain the shape of the binomial and geometric distribution.

It really depends on what you mean by “correlation” and what kind of data you have. If you really mean correlation, as in one goes up and the other goes down, is a possibility.

If Z(critical) = 2.04, what is the p-value for your test?
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  • M&M Report | Statistical Hypothesis Testing | …

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    Repeat 100 times, record the results.# of tosses until all heads or tails(Record 15 if the number 15.)

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    If so, what is it?

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Here are three experiments to illustrate when the different approaches to statistics are appropriate. In the first experiment, you are testing a plant extract on rabbits to see if it will lower their blood pressure. You already know that the plant extract is a diuretic (makes the rabbits pee more) and you already know that diuretics tend to lower blood pressure, so you think there's a good chance it will work. If it does work, you'll do more low-cost animal tests on it before you do expensive, potentially risky human trials. Your prior expectation is that the null hypothesis (that the plant extract has no effect) has a good chance of being false, and the cost of a false positive is fairly low. So you should do frequentist hypothesis testing, with a significance level of 0.05.

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A Bayesian would insist that you put in numbers just how likely you think the null hypothesis and various values of the alternative hypothesis are, before you do the experiment, and I'm not sure how that is supposed to work in practice for most experimental biology. But the general concept is a valuable one: as Carl Sagan summarized it, "Extraordinary claims require extraordinary evidence."

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Thanks, Todd. I will get to work fixing the broken link. In the meantime, it’s redirecting to the correct page. I can see how a book on the notation would be useful but whew…what a huge endeavor that would be! I have added a couple hundred articles to this site on definitions and I have tried to define statistics terms in plain English. I hope you find the site content to be a helpful addition to the book. Regards, S.

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The book itself is not what I had hoped for. However, the price (one night at the honky tonk drinking beer) is worth the risk and I’m hoping there’s some useful content.

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