Midterm Practice Questions #1 Answers

Q1. Which of the following levels of measurement is characterized by having a true zero point and equal intervals between adjacent values? A. Nominal B. Ordinal C. Interval D. Ratio Answer: D. Ratio Explanation: The ratio level of measurement not only has equal intervals between adjacent values but also possesses a true zero point, which means that the value of zero indicates the absence of the attribute being measured. Nominal and ordinal levels do not have equal intervals between adjacent values, and the interval level lacks a true zero point. Q2. Which of the following measures of central tendency is most appropriate for ordinal data? A. Mean B. Median C. Mode D. All of the above Explanation: For ordinal data, which has a clear ordering of values but unknown equal intervals between adjacent values, the median (the middle score in a ranked order of values) is the most appropriate measure of central tendency. The mean requires numerical and interval data since it involves mathematical operations based on the assumption of equal intervals. While the mode (most frequently occurring value) can be computed, it is not as informative about the central tendency for ordinal data as the median. Q3. Which of the following is a measure of spread or dispersion in a data set? A. Mean B. Mode C. Standard Deviation D. Median Explanation: The standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of values. Mean, mode, and median are measures of central tendency and do not provide information about the spread of the data. Q4. In a normally distributed data set, approximately what percentage of values lie within one standard deviation of the mean? A. 68% B. 95% C. 99% D. 50% Explanation: According to the empirical rule (or 68-95-99.7 rule), in a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations. Q5. A Z-score of 2.0 in a normal distribution corresponds to a data point that is: A. Below the mean B. Exactly at the mean C. Above the mean D. None of the above Explanation: Z-scores give us an idea of how far from the mean a data point is. A Z-score of 2.0 (C) is two standard deviations above the mean. Q6. The Z-score which corresponds to the median of a standard normal distribution is: A. 0 B. 1 C. -1 D. 0.5 Explanation: In a standard normal distribution, the mean is 0 and therefore the Z-score of the median, which is also the mean in symmetric distributions, is 0 (A). Q7. Which of the following is a common source of bias in surveys? A. Sampling Bias B. Response Bias C. Nonresponse Bias D. All of the above Answer: D. All of the above Q8. The difference between a sample estimate and the true population parameter is known as: A. Sampling Error B. Non-sampling Error C. Standard Error D. Systematic Error Explanation: Sampling Error (A) is the error caused by observing a sample instead of the whole population. This is often inevitable but can be estimated and accounted for. Q9. When the null hypothesis is true, but we reject it, we have made a: A. Type I error B. Type II error C. Sampling error D. Non-sampling error Explanation: A Type I error (A) occurs when we incorrectly reject a true null hypothesis, often equated with a "false positive". Q10. The p-value in a hypothesis test signifies: A. The probability that the null hypothesis is true B. The probability of obtaining a test statistic as extreme as the one observed, assuming the null hypothesis is true C. The probability that the alternative hypothesis is true D. None of the above Explanation: The p-value represents the probability of observing a test statistic as extreme as the one calculated, under the assumption that the null hypothesis is true (B). It helps to assess the evidence against the null hypothesis. Q11. The alternative hypothesis: A. Is a statement that there is no effect B. Is a statement that there is an effect or difference C. Is always true D. Cannot be tested Explanation: The alternative hypothesis (B) asserts that there is an effect or difference and is what researchers typically aim to support. Q12. In a hypothesis test, the level of significance (alpha, α) is: A. The probability of making a Type I error B. The probability of making a Type II error C. The probability that the null hypothesis is true D. The probability that the alternative hypothesis is false Answer: A. The probability of making a Type I error Q13. Which of the following statements is true regarding the relationship between confidence intervals and hypothesis tests? A. A confidence interval that contains the parameter value specified in the null hypothesis is consistent with a hypothesis test that rejects the null hypothesis B. A confidence interval that does not contain the parameter value specified in the null hypothesis is consistent with a hypothesis test that fails to reject the null hypothesis C. A confidence interval that contains the parameter value specified in the null hypothesis is consistent with a hypothesis test that fails to reject the null hypothesis D. None of the above Explanation: A confidence interval that contains the parameter value specified in the null hypothesis suggests that the observed data is consistent with the null hypothesis, hence, a hypothesis test should fail to reject the null hypothesis (C). Q14. When would a researcher use a one-tailed test? A. When they expect the effect to be in a specific direction B. When they are unsure of the direction of the effect C. Always D. Never Explanation: One-tailed tests are used when the researcher has a specific hypothesis about the direction of the effect (either an increase or a decrease). Q15. A two-tailed test is performed when: A. There is a hypothesis about the direction of the effect B. The test statistic is not distributed normally C. There is no specific hypothesis about the direction of the effect D. Only positive changes are expected Explanation: Two-tailed tests are employed when the researcher does not have a specific expectation about the direction of the relationship and is interested in changes in both directions. Q16. Which of the following is NOT a type of reliability? A. Test-retest B. Internal consistency C. External D. Face Explanation: Face refers to Face validity which is not a measure of reliability. The other options are all measures of reliability test-retest reliability, internal consistency which is measured by Cronbach’s alpha and external reliability Q17. What does a Cronbach's Alpha value of 0.7 typically indicate in psychological research? A. Unacceptable reliability B. Acceptable reliability C. Excellent reliability D. No reliability Explanation: A Cronbach's Alpha of 0.7 is typically considered the lower limit for acceptable reliability in psychological and social science research. Q18. Construct validity refers to: A. The degree to which a test measures what it claims to measure B. The consistency of a test result over time C. The ability of a test score to predict future performance D. The degree to which a test appears to measure what it intends to measure Explanation: Construct validity is concerned with the extent to which a test accurately measures the construct it is intended to measure. Q19. Face validity is assessed by: A. Statistical methods B. Judging if the test looks like it measures what it is supposed to C. Testing the measure against a criterion D. Using the measure in different contexts Explanation: Face validity involves a subjective assessment of whether the test appears (at face value) to measure the construct it’s intended to measure. Q20. Parametric tests generally require: A. Data measured at the nominal level B. Data that is normally distributed C. No assumptions about data distribution D. Use with ordinal data Explanation: Parametric tests often require certain assumptions about data properties, like data being normally distributed, to be met. Q21. Why might a researcher choose to use a non-parametric test? A. The data is normally distributed B. The sample size is large C. The data violates normal distribution assumptions D. The data is measured at an interval or ratio scale Explanation: Non-parametric tests are often chosen when the data does not meet the normal distribution assumptions required for parametric tests, and/or when data is ordinal or nominal. Q22. The Student's t-test is an example of a: A. Non-parametric test B. Parametric test C. Unreliable test D. Test of normality Explanation: The Student's t-test is a parametric test as it assumes that the populations the samples come from are normally distributed. Q23. The Mann-Whitney U test is utilized when: A. Comparing means of two independent samples B. Comparing means of two related samples C. Comparing the medians of two independent samples D. Calculating the mode of two samples Explanation: The Mann-Whitney U test is a non-parametric test used to determine whether there is a difference between two independent samples. It compares the medians rather than the means of the two samples. Q24. A paired samples t-test is used when: A. Samples are independent B. Samples are dependent C. Population variance is known D. Comparing more than two samples Explanation: A paired samples t-test is used when the samples are dependent, meaning that there is a natural pairing of observations, such as in before-and-after observations. Q25. Independent samples are characterized by: A. Data points in one sample affecting the other B. Pairing between the two sets of scores C. No relationship between the two sets of scores D. High correlation between the two sets of scores Explanation: Independent samples refer to datasets where the measurements taken on one group have no effect on the measurements of the other group. Q26. The Central Limit Theorem (CLT) states that: A. The distribution of sample means approaches a normal distribution as n increases B. The population mean equals the sample mean C. The sample variance equals the population variance D. The distribution of sample medians is always normal Explanation: The CLT tells us that, given a sufficiently large sample size, the sampling distribution of the mean will be approximately normal, regardless of the population distribution. Q27. The Central Limit Theorem is crucial for: A. Justifying the use of normal distribution in inferential statistics B. Calculating the mode C. Measuring nominal data D. Ensuring that population distributions are normal Explanation: The CLT provides a foundation for making inferences about the population using the normal distribution when considering sample means, even when the population distribution is not normal. Q28. Which measure of variability is influenced most by extreme scores? A. Range B. Variance C. Standard deviation D. Interquartile range Explanation: The range, calculated as the difference between the maximum and minimum scores, is highly sensitive to extreme scores because it only considers the two most extreme values in the data set. Q29. In a normal distribution, approximately what percentage of scores fall within ±2 standard deviation from the mean? A. 68% B. 95% C. 99% D. 50% Explanation: According to the empirical rule (or 68-95-99.7 rule), approximately 95% of the data falls within one standard deviation of the mean in a normal distribution. Q30. A negatively skewed distribution has: A. A tail on the right side B. A tail on the left side C. Symmetrical tails on both sides D. No tails Explanation: A negatively skewed (or left-skewed) distribution has a tail on the left side, indicating that the left tail is longer or the mass of the distribution is concentrated on the right. Q31. Which of the following is a property of a normal distribution? A. It is always symmetric B. It is always skewed to the right C. It always has a peak at the right tail D. It is bimodal Explanation: A normal distribution is symmetric, meaning that the left and right halves of the distribution are mirror images of each other. Q32. A Z-score of 0 indicates that the raw score is: A. Above the mean B. Below the mean C. Equal to the mean D. At the tail of the distribution Explanation: A Z-score of 0 means the original score is exactly at the mean of the distribution, as Zscores represent the distance between a raw score and the mean in units of standard deviation. Q33. The concept that every possible sample of a given size has a certain probability of being selected is known as: A. Random error B. Random sampling C. Sampling error D. Sampling distribution Explanation: Random sampling refers to the principle that every possible sample of a given size has an equal chance of being selected, ensuring the representativity of the sample. Q34. Standard deviation is: A. The average of squared deviations from the mean B. The square root of the variance C. The square of the variance D. Equal to the mean explanation: The standard deviation is defined as the square root of the variance, providing a measure of spread or dispersion in the same units as the original data. Q35. The mean and median are equal in a: A. Right-skewed distribution B. Normal distribution C. Left-skewed distribution D. Bimodal distribution Explanation: In a normal distribution, it is symmetric; hence, the mean and median are equal and located at the center of the distribution. Q36. In the context of the Central Limit Theorem, as the sample size increases: A. The sample means become more skewed B. The sample means become less normally distributed C. The sample means become more normally distributed D. The sample means always equal the population mean Explanation: The Central Limit Theorem states that with an increase in sample size, the distribution of the sample mean approaches a normal distribution, even if the underlying population distribution is not normal. Q37. What does variance measure? A. The average of the data B. The spread of data from the mean C. The middle score of the data D. The most frequent score in the data Explanation: Variance quantifies the dispersion of data points from the mean, considering the squared deviations of data points from the mean and averaging them. Q38 Dependent samples could arise from: A. Random assignment of subjects to two groups B. Matching subjects on a variable C. Pairing observations in a before-and-after scenario D. Both B and C Explanation: Dependent samples may arise when subjects are matched on a variable (such as age or gender) or when observations are paired, such as in a pre-test and post-test (before-and-after) design, thereby indicating a relationship or dependency between the measurements. Q39. What is a Type I error? A. Failing to reject a false null hypothesis B. Rejecting a true null hypothesis C. Accepting a true null hypothesis D. Rejecting a false null hypothesis Explanation: A Type I error occurs when a true null hypothesis is incorrectly rejected. It is often denoted by the Greek letter alpha (α) and is related to the level of significance used in testing the hypothesis. Q40. What does p-value signify in hypothesis testing? A. Probability of Type I error B. Probability of Type II error C. Probability of obtaining a sample statistic as extreme, assuming the null hypothesis is true D. Probability of obtaining a sample statistic as extreme, assuming the null hypothesis is false Explanation: The p-value indicates the probability of observing a sample statistic as extreme or more extreme by random chance, under the assumption that the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis. Q41. Which of the following is a characteristic of a parametric test? A. It makes no assumptions about the population distribution B. It requires interval or ratio data C. It is suitable for nominal or ordinal data D. It is non-sensitive to outliers Explanation: Parametric tests usually require interval or ratio data and make certain assumptions about the properties of the population distribution (such as normality). They tend to be more powerful and precise than non-parametric tests when their assumptions are met. Q42. A Type II error occurs when: A. A false null hypothesis is rejected B. A false null hypothesis is not rejected C. A true null hypothesis is rejected D. A true alternative hypothesis is accepted Explanation: A Type II error, denoted by beta (β), occurs when a false null hypothesis is not rejected, meaning that the researcher fails to recognize an effect or difference that actually exists. Q43. Reducing the risk of a Type II error will: A. Increase the power of a test B. Decrease the power of a test C. Have no effect on the power of a test D. Always increase the risk of a Type I error Explanation: The power of a test, which is the probability of correctly rejecting a false null hypothesis, increases as the risk of a Type II error decreases because power and Type II error are inversely related (Power = 1 - β). Q44. Which of the following best defines bias in statistical estimation? A. The variability of an estimator from sample to sample B. The difference between the expected value of an estimator and the parameter it estimates C. The sum of the expected value and the actual value D. The overall accuracy of a model prediction Explanation: Bias refers to the systematic, consistent deviation of the estimator from the parameter it is supposed to estimate. It indicates how off the expected values are from the true parameter. Q45. Which statement is true regarding a Type II error? A. It occurs when a false null hypothesis is rejected B. It is denoted by the symbol alpha (α) C. It occurs when a false null hypothesis is not rejected D. It involves rejecting a true null hypothesis Explanation: A Type II error, symbolized as beta (β), transpires when a false null hypothesis is not rejected, meaning an existing effect or difference was overlooked. Q46. The probability of making a Type II error is denoted by: A. α (Alpha) B. β (Beta) C. ρ (Rho) D. σ (Sigma) Explanation: Beta (β) symbolizes the probability of committing a Type II error, which involves failing to reject a false null hypothesis. Q47. Failing to reject the null hypothesis implies: A. There is conclusive evidence that the null hypothesis is false B. There is sufficient evidence to prove the null hypothesis true C. There is not enough evidence to show that the alternative hypothesis is true D. The research is invalid Explanation: Failing to reject the null hypothesis simply indicates a lack of sufficient evidence to support the alternative hypothesis; it does not confirm the truth of the null hypothesis. Q48. If a measurement process consistently underestimates the true value, it exhibits: A. Random Error B. Systematic Bias C. Sampling Error D. Non-Sampling Error Explanation: Systematic bias is when a measurement process consistently overestimates or underestimates the true value, reflecting a consistent, systematic deviation from the actual value. Q49. Random errors occur due to: A. Fluctuations and inconsistencies in measurements B. Consistent miscalculations in measurements C. Mistakes made by researchers D. Biased sampling methods Explanation: Random errors emerge unpredictably due to fluctuations and inconsistencies in measurements and are not systematically biased in one direction or the other. Q50. Which of the following variables is best described as being measured at the ordinal level? A. The number of books read in a month B Patient satisfaction ratings (Very Unsatisfied, Unsatisfied, Neutral, Satisfied, Very Satisfied) C. Temperature in Celsius D. Gender Explanation: Ordinal data involves categories with a meaningful order, but the intervals between the category levels are not assumed to be equal. Patiient satisfaction ratings present an inherent order (Unsatisfied < Neutral < Satisfied etc), but the difference between these levels is not quantifiable, making it ordinal.