True false exam 3
Chapter 10 If the null hypothesis is not rejected, there is strong statistical evidence that the null hypothesis is true.
False
Chapter 12 The t confidence interval formula for estimating mu should only be used when the population being sampled is at least approximately normally distributed.
false
Chapter 13 The large sample z test for mu1-mu2 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
Chapter 13 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
Chapter 12 A t curve is bell-shaped like the z curve but is less spread out.
false
Chapter 13 When testing a hypothesis 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
A type II error is made by failing to reject a false null hypothesis Chapter 10
true
All other things being equal, choosing a smaller value of alpha will increase the probability of making a type II error. Chapter 10
true
Chapter 12 For n sufficiently large, the distribution of x:bar -mu,p/ sigma,p is approximately a standard normal distribution.
true
Chapter 12 The confidence interval formula for estimating mu when n is large is based on the Central Limit Theorem.
true
Chapter 12 As n grows larger, the mean of the sampling distribution of x:bar gets closer to mu.
false
Chapter 12 The distribution of x:bar will always have the same shape as the distribution of the population being sampled.
false
Chapter 12 The standard error of x:bar is s.
false
Chapter 13 for two independent samples sigma[ x:bar 1- x:bar 2] = sqrt [sigma^2 #1/n1] - sqrt [ [sigma^2 #2/n2]
false
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. Chapter 11
false
The power of a test is the probability of failing to reject the null hypothesis. Chapter 10
false
beta is called the observed significance level Chapter 10
false
The level of significance of a test is the probability of making a type I error, given that the null hypothesis is true Chapter 10
true
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 Chapter 11
true
When constructing a confidence interval for the difference of two population proportions, the appropriate standard error of p^1 - p^2 is sqrt of [p^1(1-p^)/n1] + sqrt [p^2(1-p^)/n2] Chapter 11
true
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. Chapter 11
false
p^1 - p^2 is a biased estimator of p1 - p2 Chapter 11
false
Chapter 12 The distribution of x:bar is normal if the population is normal.
true
Chapter 12 The standard deviation of the distribution of x:bar decreases as n increases
true
Chapter 12 The width of the one-sample confidence interval for mu decreases as the sample size grows larger
true
Chapter 13 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 mu1 - mu2
true
Chapter 13 x:bar 1- x:bar 2 is an unbiased statistic that is used to estimate mu1- mu2
true
Chapter 13 x:bar"d" and x:bar1-x:bar2 are always equal
true
For tests of hypotheses about mu, beta decreases as the sample size increases if the level of significance stays the same. Chapter 10
true
It is customary to say that the result of a hypothesis test is statistically significant when the P-value is smaller than alpha Chapter 10
true
Small P-values indicate that the observed sample is inconsistent with the null hypothesis. Chapter 10
true
The hypothesis p1=p2 is equivalent to the hypothesis p1-p2=0. Chapter 11
true
