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# \$10.001. Random sampling is selecting a sample

• From Mathematics: Statistics
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• Due on Jun. 26, 2011
• Asked on Jun 26, 2011 at 8:56:30PM

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Q:

1. Random sampling is selecting a sample from a population such that: (Points: 4) all individuals in the sample have the same probability of being selected. only those individuals in the population who are represented are selected. the probability of some individuals being selected is greater than for others. all individuals in the population have the same probability of being selected.

2. Asaad wants to show Cheryl and Cindy a card trick he has learned. He first asks Cheryl to draw one card at random from a standardized deck of 52 cards. After Cheryl's turn she replaces her card in the deck. What is the probability that Cindy will draw an ace? (Points: 4) 1/52 4/52 0.02 2%

3. What is the purpose of the critical value? (Points: 4) to define the minimum absolute z-value required for a sample to be in the region of rejection to define the minimum absolute z-value required for a sample to be out of the region of rejection to determine the likelihood that the population mean was obtained by chance to determine the value of z needed to conclude that the population mean was obtained by chance

4. A cognitive psychologist measured the average time people look at content words (e.g., ball, kick) when reading a passage. He hypothesized that the sample mean for content words would be greater than the known population mean for all words. The z-score for the sample mean was z = 3.00. With a one-tailed critical value of +1.645, what should the psychologist conclude about the sample mean? (Points: 4) The sample mean for content words is significantly greater than the known population mean of all words. The sample mean for content words is significantly less than the known population mean of all words. The sample mean for all words is significantly greater than the known population mean of content words. The sample mean for all words is significantly less than the known population mean of content words.

5. If we decide to reject the idea that a sample represents a particular population because the sample mean lies within the region of rejection, ______. (Points: 4) although the probability is low, our decision may be wrong. the probability is high that our decision is wrong. we cannot know the probability of our decision being wrong. the probability of our decision being correct is 0.05.

6. Which of the following assumptions is common to all parametric statistics? (Points: 3) There are no common assumptions. The population of dependent scores, regardless of what kind of scores they are, forms a normal distribution. The dependent scores must be interval or ratio scores, regardless of whether the population of scores forms a normal distribution. The population of dependent scores, which must be interval or ratio scores, forms a normal distribution.

7. Which of the following is correct regarding alternative hypotheses? (Points: 3) There are alternatives to the experimental hypotheses. They state the predicted relationship if the sample statistic does not fall in the region of rejection. They describe the population parameters represented by the sample data if the predicted relationship exists. They describe the population parameters represented by the sample data if the predicted relationship does not exist.

8. When statisticians report that the results from an experiment are significant, this means the results: (Points: 3) are scientifically important. are too unlikely to accept as a sampling error. differ from what was predicted by the experimental hypothesis. do not differ from what was predicted by the null hypothesis.

9. Which of the following statements is true when H0 is not rejected? (Points: 3) The experiment worked. The data reflect a relationship found in nature. The data do not provide sufficient evidence of a relationship in nature. The data prove that the manipulation did not work.

10. Which of the following represents a Type I error? (Points: 3) We say that something works when it really does. We say that something works when it really doesn't. We say that something doesn't work when it really does. We say that something doesn't work when it really doesn't.