What increases the likelihood of rejecting the null hypothesis?
When we increase the sample size, decrease the standard error, or increase the difference between the sample statistic and hypothesized parameter, the p value decreases, thus making it more likely that we reject the null hypothesis.
Which combination of factors is most likely to result in a statistically significant outcome?
In general, a sample with large variance is more likely to produce a significant result than a sample with small variance.
Which set of characteristics will produce the smallest value for estimated standard error?
Answer and Explanation: The scenario that will result in the smallest value for the standard error is option A: A large sample size and a small sample variance.
What is the effect of increasing the sample variance?
Generally speaking, increasing the sample variance implies increasing its square-root the sample std dev, which in turn, increases the estimated std error of the sample mean.
How does sample size affect power?
This illustrates the general situation: Larger sample size gives larger power. The reason is essentially the same as in the example: Larger sample size gives a narrower sampling distribution, which means there is less overlap in the two sampling distributions (for null and alternate hypotheses).
What are the four factors that affect the power of a test?
The 4 primary factors that affect the power of a statistical test are a level, difference between group means, variability among subjects, and sample size.
What will be the standard error of the mean result when using a small sample compared to a large sample?
The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value. The standard error is considered part of inferential statistics.
What is the effect of a large value for the estimated standard error?
It would increase the likelihood of rejecting the null and increase the risk of a Type I Error.
What happens to the t statistic when n becomes smaller?
t-statistic Since the square root of n is the denominator of that fraction, as it gets bigger, the fraction will get smaller. However, this fraction is, in turn, a denominator. As a result, as that denominator gets smaller, the second fraction gets bigger. Thus, the t-value will get bigger as n gets bigger.
Why is a smaller variance better?
A small variance indicates that the data points tend to be very close to the mean, and to each other. A high variance indicates that the data points are very spread out from the mean, and from one another.
What would be the result of setting an alpha level extremely small?
What would be the result of setting an alpha level extremely small? a. There would be almost no risk of a Type I error.
What affects variance?
Properties of Variances If a random variable X is adjusted by multiplying by the value b and adding the value a, then the variance is affected as follows: Since the spread of the distribution is not affected by adding or subtracting a constant, the value a is not considered.