<<1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950>> 1. What does the term "population" refer to in statistics?A subset of a larger groupAll items being studiedThe mean, median, and mode of a sampleThe characteristics of a sampleQuestion 1 of 50 2. How is a sample different from a population?A sample includes only mean, median, and modeA sample is a smaller version of a populationA sample represents the entire group being studiedA sample is unrelated to statistical analysisQuestion 2 of 50 3. What are statistics in the context of sampling and populations?Characteristics of a populationLower case Roman letters used in data analysisMeasures like mean, median, and mode describing a populationA subset chosen from the populationQuestion 3 of 50 4. Which term denotes characteristics of a sample rather than a population?ParametersStatisticMean, median, and modeGreek or capital lettersQuestion 4 of 50 5. How are parameters different from statistics?Parameters use lower case Roman lettersStatistics describe a populationParameters are characteristics of a sampleParameters are population characteristics while statistics are sample characteristicsQuestion 5 of 50 6. What characterizes random or probability sampling?Sampling based on the researcher's knowledgeUnequal probability of sample selectionEach sample having an equal chance of selectionChoosing samples only from interested groupsQuestion 6 of 50 7. How is non-random or judgment sampling different from random sampling?It involves biased sample selectionSamples are chosen based on equal probabilitiesSamples are chosen solely on the researcher's judgmentIt aims for an unbiased representation of the populationQuestion 7 of 50 8. What defines biased sampling?Selection based on equal probabilitiesRandom sampling from interested groupsChoosing samples without researcher judgmentSamples chosen from specific, affected groupsQuestion 8 of 50 9. In the given example of conducting an opinion survey for the women's bill, what type of sampling is being used?Random or probability samplingNon-random or judgment samplingBiased samplingStratified samplingQuestion 9 of 50 10. What characterizes simple random sampling?Unequal probability of sample selectionSelection based solely on the researcher's judgmentEach possible sample having an equal probability of selectionSamples chosen from specific, affected groupsQuestion 10 of 50 11. In simple random sampling, what distinguishes sampling with replacement from sampling without replacement?Sampling with replacement involves biased selectionSampling without replacement allows items to be picked againSampling with replacement ensures each item is included in the sampleSampling without replacement does not permit items to be picked againQuestion 11 of 50 12. What is an example of simple random sampling?Interviewing employees based on their departmentsSelecting employees randomly from different citiesChoosing employees based on their performanceDrawing employee names from a box without biasQuestion 12 of 50 13. What is the fundamental principle behind simple random sampling?Selection based on the researcher's knowledgeEnsuring each sample is biased towards certain groupsEqual probability of selection for all possible samplesIncluding only items that fit specific criteriaQuestion 13 of 50 14. What defines systematic sampling?Random selection of elements without a patternSelecting elements at irregular intervalsUniform interval selection of elements from a populationRandomly choosing elements from specific groupsQuestion 14 of 50 15. How does systematic sampling differ from simple random sampling?Systematic sampling ensures each sample has an equal chance of selectionSimple random sampling selects elements at irregular intervalsSystematic sampling involves random starting points for selectionSimple random sampling has a lower cost and is less time-consumingQuestion 15 of 50 16. In the provided example of interviewing every twentieth student on a college campus, what method of sampling is being used?Simple random samplingNon-systematic samplingSystematic samplingStratified samplingQuestion 16 of 50 17. What characterizes the intervals in systematic sampling?Irregular and unpredictable intervalsIntervals based on the researcher's judgmentUniform and consistent intervalsIntervals based on the population sizeQuestion 17 of 50 18. What defines stratified sampling?Selection of elements at random without any groupingDivision of the population into homogeneous groupsChoosing elements based on a specified patternRandom selection without considering population divisionsQuestion 18 of 50 19. What is a key characteristic of stratified sampling regarding the selection of elements from different strata?Selection based solely on the researcher's judgmentEach stratum contributes equally to the sampleUnequal representation of each stratum in the sampleEqual number of elements chosen from each stratumQuestion 19 of 50 20. When is stratified sampling considered appropriate?When the population is randomly distributedWhen the population is divided into groups of varying sizesWhen the population is small and homogenousWhen the population lacks any clear divisionsQuestion 20 of 50 21. What is an example of potential stratification for sampling?Selecting individuals based on their preferencesChoosing elements at random from a homogeneous groupDividing the population based on various categories like age or incomeRandomly assigning weights to different elements in the populationQuestion 21 of 50 22. What characterizes cluster sampling?Random selection of individual elements from the populationDivision of the population into homogeneous groupsSelection of random groups (clusters) from the populationEqual representation of every cluster in the sampleQuestion 22 of 50 23. What assumption is made in cluster sampling?Each individual in the population is sampledClusters represent the population as a wholeAll clusters have equal variation within themselvesSmall clusters are more representative than larger onesQuestion 23 of 50 24. In the example provided regarding determining the average number of television sets per household in a city, what sampling method is being described?Stratified samplingSimple random samplingCluster samplingNon-random samplingQuestion 24 of 50 25. How does cluster sampling differ from stratified sampling?Cluster sampling is used when groups have small variation within themselvesStratified sampling involves dividing the population into clustersCluster sampling involves random selection from homogeneous groupsStratified sampling assumes large variation within each groupQuestion 25 of 50 26. What best defines a sampling distribution?Distribution of individual values in a populationProbability distribution of sample statistics from various random samplesFrequency distribution of a single sample's valuesDistribution of sample means in a populationQuestion 26 of 50 27. Which distribution represents all possible values of a sample statistic drawn from a population?Population distributionIndividual sample distributionSampling distributionDescriptive distributionQuestion 27 of 50 28. What does the standard error of the mean measure?Variability in sample proportionsExtent of variability in observed means due to chance in samplingVariability in population meansThe difference between sample and population proportionsQuestion 28 of 50 29. How is the standard error of the proportion defined?Standard deviation of the distribution of sample meansStandard deviation of the distribution of sample proportionsVariability in population proportionsVariability in observed means due to chanceQuestion 29 of 50 30. What contributes to the variability in sampling statistics, as per the concept of standard error?Difference between sample and population meansSampling error due to chanceDifference between sample and population proportionsVariability in population meansQuestion 30 of 50 31. What does the standard deviation of the sampling distribution of means measure?Variability due to sampling errorDifference between sample and population meansVariability in observed proportionsExtent of variability in population meansQuestion 31 of 50 32. What term is used to describe the standard deviation of the distribution of a sample statistic?Variance of the sampleStandard error of the statisticSampling deviationPopulation errorQuestion 32 of 50 33. Which statement accurately represents the Central Limit Theorem (CLT)?The mean of the sampling distribution equals the population mean only for very large sample sizes.The sampling distribution of the mean always approaches normality, irrespective of sample size or population distribution shape.A large sample is necessary for the sampling distribution of the mean to approach normality.The CLT implies the population mean determines the shape of the sampling distribution.Question 33 of 50 34. What is the significance of the Central Limit Theorem in statistical inference?It requires a specific sample size for the sampling distribution to be normal.It guarantees the population distribution's normality for any sample size.It allows the use of sample statistics to make inferences about population parameters, regardless of population distribution shape.It ensures that the mean of the sampling distribution equals the population mean.Question 34 of 50 35. How does the Central Limit Theorem relate to the normal distribution approximation for the sampling distribution of the mean?It requires a sample size of at least 30 for the sampling distribution to be nearly normal.The normal distribution approximation is reliable only for sample sizes larger than 30.The CLT suggests the sampling distribution can approach normality with sample sizes even smaller than 30.The normal distribution approximation is irrelevant to the CLT's principles.Question 35 of 50 36. Which of the following is a method of selecting samples from a population?Judgement samplingRandom samplingProbability sampling(a) and (b) but not (c)Question 36 of 50 37. the pair of symbols that best completes this sentence: - is a parameter, whereas - is a statistic.N, mN, nS, s(b) and (c) but not (a)Question 37 of 50 38. In random sampling, we can describe mathematically how objective our estimates are. Why is this?We always know the chance that any population element will be included in the sampleEvery sample always has an equal chance of being selectedAll the samples are exactly the same size and can be counted(a) and (b) but not (c)Question 38 of 50 39. Suppose you are performing stratified sampling on a particular population and have divided it into strata of different sizes. How can you now make your sample selection?Select at random an equal number of elements from each stratumDraw equal numbers of elements from each stratum and weigh the resultsDraw numbers of elements from each stratum proportional to their weights in the population(b) and (c) onlyQuestion 39 of 50 40. In which of the following situations would o, =o/ In be the correct formula to use for computingSampling is from an infinite populationSampling is from a finite population with replacementSampling is from a finite population without replacement(a) and (b) onlyQuestion 40 of 50 41. The dispersion among sample means is less than the dispersion among the sampled items themselves becauseEach sample is smaller than the population from which it is drawnVery large values are averaged down, and very small values are averaged upThe sampled items are all drawn from the same populationNone of theseQuestion 41 of 50 42. Suppose that a population with N= 144 has m = 24. What is the mean of the sampling distribution of the mean for samples of size 25?2424.8Cannot be determined from the information givenQuestion 42 of 50 43. The central limit theorem assures us that the sampling distribution of the meanIs always normalIs always normal for large sample sizesApproaches normality as sample size increasesAppears normal only when N is greater than 1,000Question 43 of 50 44. Suppose that, for a certain population, s is calculated as 20 when samples of size 25 are taken and as 10 when samples of size 100 are taken. A quadrupling of sample size, then, only halved sx. We can conclude that increasing the sample size isAlways cost-effectiveSometimes cost-effectiveNever cost-effectiveNone of theseQuestion 44 of 50 45. What must be the value of s for this infinite population for the previous question?1,000500377.5100Question 45 of 50 46. The finite population multiplier does not have to be used when the sampling fraction isGreater than 0.05Greater than 0.50Less than 0.50None of theseQuestion 46 of 50 47. The standard error of the mean for a sample size of two or more isAlways greater than the standard deviation of the populationGenerally greater than the standard deviation of the populationUsually less than the standard deviation of the populationNone of theseQuestion 47 of 50 48. A border patrol checkpoint that stops every passenger van is usingSimple random samplingSystematic samplingStratified samplingComplete enumerationQuestion 48 of 50 49. In a normally distributed population, the sampling distribution of the meanIs normally distributedHas a mean equal to the population meanHas a standard deviation equal to the population standard deviation divided by the square root of the sample sizeAll of the aboveQuestion 49 of 50 50. The central limit theoremRequires some knowledge of the frequency distributionPermits us to use sample statistics to make inferences about population parametersRelates the shape of a sampling distribution of the mean to the mean of the sampleRequires a sample to contain fewer than 30 observationsQuestion 50 of 50 Loading...
