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Which of the following can invalidate significance test results?
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A medical researcher is working on a new treatment for a certain type of cancer. The average survival time after diagnosis on the standard treatment is two years. In an early trial, she tries the new treatment on three subjects who have an average survival time after diagnosis of four years. Although the survival time has doubled, the results are not statistically significant even at the 0.10 significance level. What is the best explanation?
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We are studying an interesting and potentially important question. If we have failed to meet the standard significance level but from the information provided by the P-Value, we can see that the result just barely missed the standard...
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A study was conducted to determine the effect of the addition of a particular supplement to a well-known LDL lowering cholesterol drug. 5000 randomly selected patients were studied on this combination of drugs over a one-year period. An increased reduction of 5 mg/dL of LDL cholesterol was achieved with the combination of drugs relative to the well-known drug alone. The reduction resulted in a P-value of 0.001.
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In a significance test, the null hypothesis:

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When estimating the population proportion, a conservative interval is narrower than an interval where we estimate the standard error using the sample proportion.

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Imagine I have estimated a 95% confidence interval for the mean height of your class for samples of size 10 and find it is between 1,65m and 1,75m. Thus, the confidence interval has a width of 10 cm and the margin of error is of 5 cm. 

Mark the correct interpretation:

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When the variance of data in the populationis larger (all else being equal)...

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When we are conducting a hypothesis test, we assume that the alternative hypothesis is true while we are doing the test, and then come to a conclusion on the basis of the figures that we calculate during the test.

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The test statistic measures: 

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