False The F test assumes that the sampled populations are not normal. A. True B. False The F test for equality of two variances tests whether the ratio of the sample variances differ significantly from 1. A.
Understanding the Basis of Statistical Power in Psychology Research Work What is Statistical Power? The power of a statistical test for a null hypothesis is the probability of having the basis to correctly reject a false null hypothesis (Greene, 2000). Statistical power is the probability of detecting an effect if the effect actually exists or the probability that the test will lead to a conclusion that the effect actually exists (High, 2000 & Cohen, 1988, p. 4). It is also the ability of the test to report a statistically significant effect where an actual effect of a given magnitude exists. In simple terms, statistical power is the likelihood that a researcher will discover an effect of a certain size in a statistical test no matter how small.
Statistics about the actual population rather than the target population B. Non-response bias C. Inability to perform inferential statistics D. Probability sampling When every member of a population has the chance of being selected based on the probability, or frequency, of its representation in that population, you are using which type of sampling? A. Quota sample B. Census sample C. Convenience sample D. Random sample Which of the following statements is NOT true? A. Estimating parameters is an important aspect of descriptive statistics. B.
Is it reasonable to assume that the weights are normally distributed? Why? b) Find the mean and standard deviation of your sample. c) Is there a high probability that the mean and standard deviation of your sample are consistent with those found in previous studies? Explain your reasoning.
A null hypothesis is the hypothesis that there is no significant difference between specific populations, where observed differences are due to errors. The Hardy-Weinberg Theory is a null hypothesis and that is what we tested in this experiment. The only time a null hypothesis can be accepted or rejected but cannot be proven. It may be quantified as true, but it cannot be proven. The reason for this is because in tests like these observed differences are usually due to chance differences in sampling.
For what values of t will the null hypothesis not be rejected? a) To the left of -1.645 or to the right of 1.645 b) To the left of -1.345 or to the right of 1.345 c) Between -1.761 and 1.761 d) To the left of -1.282 or to the right of 1.282 2. Which of the following is a characteristic of the F distribution? a) Normally distributed b) Negatively skewed c) Equal to the t-distribution d) Positively skewed Complete Answers here QNT 561 3. For a chi-square test involving a contingency table, suppose the null hypothesis is rejected.
For what values of t will the null hypothesis not be rejected? a) To the left of -1.645 or to the right of 1.645 b) To the left of -1.345 or to the right of 1.345 c) Between -1.761 and 1.761 d) To the left of -1.282 or to the right of 1.282 2. Which of the following is a characteristic of the F distribution? a) Normally distributed b) Negatively skewed c) Equal to the t-distribution d) Positively skewed Want help? Download now QNT 561 3.
Selected Answer: False Question 8 The Delphi develops a consensus forecast about what will occur in the future. Selected Answer: True Question 9 __________ is a measure of dispersion of random variable values about the expected value. Selected Answer: Standard Deviation Question 10 In Bayesian analysis, additional information is used to alter the __________ probability of the occurrence of an event. Selected Answer: Marginal Question 11 The __________ is the maximum amount a decision maker would pay for additional information. Selected Answer: Expected Value of Perfect Information Question 12 In the Monte Carlo process, values for a random variable are generated by __________ a probability distribution.
| The questions on a survey may be poorly constructed. | Correlational Research | Clarifies the relationship between variables that cannot be examined by other research methods. | This method does not permit conclusion regarding cause and effect. | Experimental Research | Allows to opportunity to draw case and effect relationships. | The type of setting may influence the subjects’ behavior.Researcher could be bias.
Chapter 3 Risk Identification and Measurement I. Multiple Choice 1. A listing of a random variable’s possible outcomes and the respective probabilities of those outcomes is called the: a. expected value b. standard deviation c. probability distribution d. correlation Answer: c Type: K 2. Multiplying each possible outcome of a random variable by its probability of occurrence, and then adding up these results will be equal to the random variable’s: a. expected value b. standard deviation c. probability distribution d. correlation Answer: a Type: K 3. The graph above depicts the probability distributions for risks A and B.