Descriptive and Inferential statistics
Quiz by Bethan Bissett
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10 questions
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- Q1What measures the central tendency of a data set?Standard deviationVarianceRangeMean30s
- Q2What is the difference between the highest and lowest value in a data set called?VarianceStandard deviationMeanRange30s
- Q3What is a measure of how much the values in a data set differ from the mean?RangeMedianMeanVariance30s
- Q4What is the term used to describe a value that occurs most frequently in a data set?MeanMaximumModeMedian30s
- Q5What is the term used to describe the middle value in a sorted data set?MedianMinimumMeanMode30s
- Q6Which of the following measures of central tendency is resistant to outliers?ModeMedianMeanRange30s
- Q7What is the purpose of inferential statistics?To make inferences about a population based on data collected from a sample.To make predictions about future events.To make inferences about a sample based on data collected from the population.To describe data collected from a sample.30s
- Q8What is the difference between a null hypothesis and an alternative hypothesis?A null hypothesis assumes there is no significant difference between two variables, while an alternative hypothesis assumes there is a significant difference.A null hypothesis assumes there is no difference between two populations, while an alternative hypothesis assumes there is a difference.A null hypothesis assumes there is a difference between two populations, while an alternative hypothesis assumes there is no difference.A null hypothesis assumes there is a significant difference between two variables, while an alternative hypothesis assumes there is no significant difference.30s
- Q9What is the difference between Type I and Type II errors?Type I error is rejecting a true null hypothesis, while Type II error is failing to reject a false null hypothesis.Type I error is failing to reject a true null hypothesis, while Type II error is rejecting a false null hypothesis.Type I error is rejecting a true alternative hypothesis, while Type II error is failing to reject a false alternative hypothesis.Type I error is failing to reject a true alternative hypothesis, while Type II error is rejecting a false alternative hypothesis.30s
- Q10What is the level of measurement for data that can be categorized but not ranked?RatioOrdinalIntervalNominal30s