Exploring One–Variable Data
If a dataset shows low variability, what can be inferred about the values in the dataset?
There is no relationship between the values.
Each value is significantly different from the mean.
The values are spread out over a wide range.
The values are relatively close to each other.
Which sampling method involves dividing the population into distinct groups before randomly selecting sample participants from each group?
Stratified random sampling
Cluster sampling
Simple random sampling
Convenience sampling
What term describes the difference between the highest and lowest values in a data set?
Interquartile range
Median
Range
Mode
In determining whether a diet causes weight loss over time within individuals following unique diets, what analysis best accommodates individual variation while evaluating overall effectiveness?
Mixed-effects model analysis.
Crossover experimental design analysis.
Paired sample t-test analysis.
Repeated measures ANOVA analysis.
A spinner with four equal sections labeled A, B, C, and D is spun twice; what is the probability that it lands on B at least once?
1/16
2/4
1/8
7/16
Which of the following statements about z-scores is true?
Z-scores can only be calculated for normally distributed data.
Z-scores are measures of relative position within a data set.
Z-scores are always positive values.
Z-scores are unaffected by the standard deviation of the data set.
When comparing two treatments for plant growth, what type of experimental design element might reduce variability in results due to extraneous factors?
Testing both treatments on the same type of plants at different times.
Applying both treatments randomly without any systematic method.
Choosing one treatment based on researcher preference over another treatment.
Using a control group for comparison.

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If a researcher is testing an unorthodox prediction that the average sleep duration for high school seniors is different from the commonly accepted 7 hours, which statistical test should be used?
Two-sample t-test.
Analysis of variance (ANOVA).
One-proportion z-test.
Chi-square test of independence.
If an AP Statistics student is evaluating the robustness of a linear regression model by comparing its predictive accuracy across datasets with varying levels of outliers, which method would best ensure comparability?
Comparing the R-squared values of the models fitted to each dataset directly.
Conducting a t-test for independence between residuals and predicted values in each dataset.
Using standardized residual plots for each dataset to assess deviations from the line.
Calculating and comparing mean squared errors without accounting for sample size differences.
In order to infer about population parameters based on sample statistics without making assumptions about the shape of the population distribution, which nonparametric procedure can be applied?
Bootstrap confidence interval procedure
Two-Sample T-Test
Paired Samples T-Test
One-Sample Z-Test