Question 11 pts

Which of the following is false?

Group of answer choices

There is nothing wrong with having people voluntarily respond to surveys and questionnaires.

Three methods we can use to find better samples are stratified sampling, multistage sample and simple random sampling.

A biased sample occurs when one or more parts of the population are favored over others.

A bias is when not everyone in the population has an equal chance to be a part of the sample.

A convenience sample only includes people who are easy to reach.

Question 21 pts

Which of the following isfalse?

Group of answer choices

A stratified random sample is when we take a population and divide it by similar groups of people and take a simple random sample in each group.

Stratified random sampling is good to make sure the person administering the survey gets members from each type of group.

We can just choose people at random and the sample will be random. We dont need a random digits table or a computer or a system to be random.

I cannot just stand outside and survey only the people that walk by because that would lead to a biased sample.

Insurance companies tend to get low ratings because of the voluntary bias in the way they receive customer service feedback/surveys

Question 31 pts

Which of the following isfalse?

Group of answer choices

The larger the sample, the more representative it is of the population

There is a problem of undercoverage if we conduct telephone surveys

A census surveys each and every person of a population

Some election polls are conducted online and are on a voluntary response basis so they are unbiased

Undercoverage means certain people in the population have no chance of being included in the sample

Question 41 pts

Which of the following isfalse?

Group of answer choices

The more natural variation there is, the more sampling error there will be

Sampling variation or sampling error is the variability that occurs by chance because a sample rather than an entire population is surveyed.

Poorly worded questionnaires with loaded words are examples of sampling errors

Different samples taken would lead to different sample statistics (like sample means)

Natural variation is variation that exist in the population.

Question 51 pts

Which of the following isfalse?

Group of answer choices

The sampling distribution is usually well represented by a normal distribution

We need a sample statistic (like sample mean), sample size, and sample standard deviation to make inferences about a population

We usually know the mean of the population

We take samples to make inferences about the population

The sampling distribution is less spread out than the values in the population distribution

Question 61 pts

Which of the following isfalse?

Group of answer choices

A sample will almost never be a perfect representation of a population

The mean of all the sample means will equal the mean of the population

Bigger samples lead to smaller spread in the sampling distribution

In real life, we usually take just one sample

Population distributions are usually bell shaped

Question 71 pts

Which of the following isfalse?

Group of answer choices

If the population had low variation, then the confidence interval would be narrower

A higher confidence level would lead to a narrower confidence interval

A confidence interval tells us how accurate our estimate is likely to be

The best estimate for the population mean is the sample mean

If a population had low variation, then the samples would be pretty similar to other samples

Question 81 pts

Which of the following isfalse?

Group of answer choices

Small samples vary more from each other and have less information

Larger samples would be more similar to each other

Usually a computer would help us calculate a confidence interval

We usually know the variation in the population

A 95% confidence interval means we are 95% sure the population parameter would be contained in this interval. There is a 5% chance of being wrong.

question 91 pts

Which of the following isfalse?

Group of answer choices

A 95% confidence interval tells us that if we took 100 different samples and did the math to find the confidence intervals, 95 of them would have the population parameter of interest and 5 of them could be completely wrong.

A really high confidence interval would give us a lot of useful information

The standard error tells us how spread out the sample means are for all the different samples possible.

The value of the t-statistic depends on the sample size

The standard deviation tells us an average distance from a data point to the mean

Question 101 pts

Which of the following isfalse?

Group of answer choices

A confidence interval can be calculated by taking a point estimate and adding and subtracting a margin of error

Confidence intervals help us estimate where a population parameter (like population mean) might be by using sample data

Excel calculates the last step of the confidence interval for us

When working with data, we should always look at the count to be sure we have included all the data

We get the lower bound by taking the point estimate and subtracting the margin of error

Question 111 pts

What is the confidence interval for if x= 10 and the Margin of Error is 2.

Group of answer choices

(2, 12)

(8, 10)

(10, 12)

(2, 10)

(8, 12)

Question 121 pts

Firestone Tires says their tires can last at least 50,000 miles before they need to be replaced, assuming normal driving conditions. If we wanted to verify their claim, what would the null hypothesis for the average life of the tires look like?

Group of answer choices

H0: u 50,000

H0: u = 50,000

H0: u < 50,000

H0: u > 50,000

H0: u 50,000

Question 131 pts

Which of the following isfalse?

Group of answer choices

The null hypothesis always has an "equal" sign in there somewhere

The p-value is the chances that the sample we got happened just by chance.

A null hypothesis is a claim that we assume is true but try to find evidence to disprove

Hypotheses are about samples statistics and not about population parameters

If the p-value is less than the significance level, then we reject the null hypothesis.

Question 141 pts

Which of the following isfalse?

Group of answer choices

The smaller the p-value is, the more evidence that the null hypothesis was wrong

The significance level is the percent that we are willing to incorrectly reject a true null hypothesis. It's also called alpha, or type 1 error.

If my friends and I tested our internet speed and our sample mean speed was 38 Mbps but Spectrum promised us 40 Mbps, but the p-value of our sample is .01 and the significance level was .05, then I would reject the null hypothesis that we are getting 40 Mbps and have evidence that spectrum is giving us less than what we paid for.

The smaller the p-value, the less likely it is that the results we see from the sample was pure luck.

If the p-value is large, then we reject the null hypothesis and have a statistically significant result

Question 151 pts

Which of the following isfalse?

Group of answer choices

If our samples consistently don't fit with our initial assumptions about the population then we should change our assumptions.

Type 2 error is when we do not reject H0 even though it is false

If the p-value is .03, and alpha is .01, we reject H0 and have evidence for H1

Degrees of freedom for t-statistics is sample size minus 1

If we only care about whether the population mean is higher than we think then we are using a one tailed test.

Question 161 pts

Which of the following isfalse?

Group of answer choices

The t-statistic tells us how many standard errors away our sample mean is from the hypothesis population mean.

The smaller the p-value, the weaker our evidence against the null hypothesis

The p in p-value stands for "probability"

The central limit theorem says that we can use the normal distribution to model the sampling distribution

Question 171 pts

Which of the following isfalse?

Group of answer choices

We need to run hypothesis tests to be sure that what we see in our sample isn't just a result of pure luck

The size of the sample affects the p-value

Remdesivir, an antiviral for covid, was tested in a treatment group and a control group and the difference in mortality rates (death) was found to be about 4% lower with remdesivir but with a p-value of .12. If alpha is .01, then this means remdesivir is statistically significant in reducing mortality rates. Assume the null hypothesis says that remdesivir is NOT effective. (the difference in mortality rates is zero.)

If we use a significance level of 0.05, then we will reject a true null hypothesis about 5% of the time.

A p-value of 0.00 indicates that there is very little to no chance of getting this result by chance if the null hypothesis is true.

Question 181 pts

Suppose I were looking at Firestone Tires again. Suppose I actually took a sample of 25, and had a sample mean of 49,000 miles and sample standard deviation of 1000 and plugged these numbers into the computer and found the t-statistic to be -5.0. And the one-tailed p-value to be .00002078. If I had a significance level of 1%, what would I conclude? (overstate means that the tires don't last as long as they say it should)

Group of answer choices

- I do not have enough evidence to say that Firestone overstated the durability of their tires.

- I have enough evidence to say that Firestone overstated the durability (life) of their tires. (this means the tires don't last as long as they said it would) (and we can sue them.)

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Question 191 pts

If my null hypothesis had the "=" sign in it, then I am looking at a:

Group of answer choices

One tailed test

Two tailed test

Question 201 pts

Which of the following isfalse?

Group of answer choices

If I got sick and ate a snickers bar and got better, I could say the snickers bar cured me. I don't need to use statistics to prove it.

We choose the alpha level before we even take a sample

If p-value alpha, we FAIL to reject H0

If p-value < alpha, we reject H0