Means
Why is considering variability important when interpreting a correlation coefficient between two variables?
High variability can weaken apparent relationships shown by correlation coefficients.
Variability ensures that all data pairs are equally weighted in correlation calculations.
Correlation coefficients automatically adjust for differences in variability.
Variability impacts correlation coefficient values differently for each variable.
Which of the following is a method for collecting data that could be used to determine the average height of students in a high school?
Calculating a z-score
Conducting a survey
Computing the standard deviation
Performing a hypothesis test
When comparing the variability of two distributions, which of the following measures would be most appropriate to use?
Mean
Median
Standard deviation
Mode
When selecting a procedure for inferential statistics, what is generally the first question you should ask?
Can I use a chi-square test?
Are the groups related or independent?
Do I have one or two samples?
Am I dealing with means or proportions?
When comparing two regression models using residual plots from different datasets, which pattern best indicates that one model may be more robust than another?
The model with higher variance in residuals at extreme values of predictors.
The model with randomly scattered residuals around zero without any systematic pattern.
The model with clusters of residuals far away from zero.
The model with residuals that show a clear linear trend.
To analyze if there has been an improvement over years in response times after training sessions among emergency responders, which method would be most appropriate?
One-way ANOVA comparing response times across multiple years
Paired t-test comparing response times before and after training
Z-test comparing proportions of improved vs non-improved responders
Chi-square test for independence between years and response times
When trying to determine if there's an interaction effect between two treatments using a factorial design at a significance level of with limited power due to small sample sizes, which technique would increase the sensitiv...
Increasing to 0.05 to relax type I error rate.
Using one-tailed tests instead of two-tailed tests.
Conducting post hoc tests using Bonferroni correction.
Pooling variances across groups for improved estimation.

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If researchers divide subjects into subgroups based on characteristics like age or income and then take samples from each subgroup, what is this method called?
Stratified sampling
Simple random sampling
Cluster sampling
Multi-stage sampling
If a researcher uses a 5% significance level instead of a 1% significance level to test the effectiveness of a new medication, what is the potential impact on Type I and Type II errors?
Increased probability of committing a Type II error and decreased probability of committing a Type I error.
Increased probability of committing a Type I error and decreased probability of committing a Type II error.
No change in the probabilities of committing either type of error.
Decreased probability of both Type I and Type II errors.
If a survey is conducted by asking students in the school library about their study habits, which type of bias is most likely to be present?
Measurement bias
Selection bias
Nonresponse bias
Response bias