If the null hypothesis is not rejected, there is strong statistical evidence that the null hypothesis is true.
False
A type II error is made by failing to reject a false null hypothesis.
True
The level of significance of a test is the probability of making a type I error. given that the null hypothesis is true.
True
For tests of hypotheses about m,B decreases as the sample size increases if the level of significance stays the same
True
B is called the observed significance level.
False
All other things being equal, choosing a smaller value of a will increase the probability of making a type II error
True
Small P- values indicate that the observed sample is inconsistent with the null hypothesis.
True
It is customary to say that the result of a hypothesis test is statistically significant when the P-value is smaller that a.
True
The power of a test is the probability of failing to reject the null hypothesis.
False
Two samples are said to be independent when the selection of the individuals in one sample has no bearing on the selection of those in the other sample.
True
p1 - p2 is a biased estimator of p1-p2.
False
The hypothesis p1=p2 is equivalent to the hypothesis 01-p2=0.
True
The P-value for a hypothesis test concerning the difference in two population proportions is always calculated by finding the area to the right of the test statistic, regardless of the alternative hypothesis.
False
The standard deviation of p1-p2 used in the large sample test of p1-p2 is the same as the standard deviation used in the large sample confidence interval for p1-p2.
False
The distribution of x will always have the same shape as the distribution of the population being sampled.
False
As n grows larger, the mean of the sampling distribution of x gets closer to m.
False
The standard deviation of the distribution of x decreases as n increases.
True
The distribution of x is normal if the population is normal.
True
The confidence interval formula for estimating m when n is large is based on the Central Limit Theorem.
True
The width of the one-sample confidence interval for m decreases as the sample size grows larger.
True
The t confidence interval formula for estimating m should only be used when the population being sampled is at least approximately normally distributed.
False
A t curve is bell-shaped like the z curve but is less spread out.
False
The standard error of x is s.
False
X1-X2 is an unbiased statistic that is used to estimate m1-m2.
True
The large sample z test for m1-m2 can be used as long as at least one of the two sample sizes, n1 and n2 is greater than or equal to 30.
False
When testing a hypotheses concerning the difference of two independent population means if the variance of the difference is estimated using the sample variances, the resulting test statistic has a Normal distribution.
False
The number of degrees of freedom of the two-sample t test are the same as the degrees of freedom for the paired t test statistic.
False
The number of degrees of freedom used in the two-sample t test for independent samples are the same as the degrees of freedom used in the construction of a confidence interval for M1-M2.
True
A chi-squared goodness-of-fit can be used to test hypotheses about the proportion of the population falling into each of the possible categories.
True
In order to decide whether the observed data is compatible with the null hypothesis, the observed cell counts are compared to the cell counts that would be expected when the alternative hypothesis is true.
False
The chi- squared test statistic x1, measures the extent to which the observed cell counts differ from those expected when h0 is true.
True
For the chi-squared goodness-of-fit chi-squared test, the associated P-value is the area under the appropriate chi-squared curve to the left of the calculated value of x2.
False
For a sample size n, there are n-1 degrees of freedom associated with the goodness-of-fit test statistic, x2
False
The row and column marginal totals provide information on the distribution of the observed values for each of the two variables defining the contingency table.
True
The expected cell count for the row a and column b entry in a bivariate contingency table is equal to the product of the row a and column b marginal totals.
False
The chi-squared test statistic for testing independence in a two-way tables has rc-1 degrees of freedom.
False