Question 1

A single number that is used to predict another value is a point estimate.

Accepted Answer

A point estimate is a single value used to predict or estimate another value.

Question 2

Confidence intervals that have higher % confidence are likely to encompass a wider range than one that has a lower % confidence.

Accepted Answer

This is because a higher % confidence interval requires a larger range of possible values in order to capture the true population parameter with that level of certainty.

Question 3

Confidence Intervals are created for the sole purpose of estimating the true population mean.

Accepted Answer

Confidence intervals are created for estimating the true population parameter, which could be mean, proportion, standard deviation, or any other parameter of interest.

Question 4

Precision is best described as when the interval estimate captures the true estimated value.

Accepted Answer

Precision refers to the degree to which the measurements or observations of a sample are clustered around the true value. Accuracy, on the other hand, refers to the closeness of measurement to the true value. Therefore, precision is not related to interval estimates.

Question 5

The null and alternate hypotheses generally are about different distributions.

Accepted Answer

The null and alternative hypotheses are set up to test whether different distributions are statistically significant. The null hypothesis assumes that there is no difference between the distributions being compared, while the alternative hypothesis assumes that there is a significant difference. Therefore, they are about different distributions.

Question 6

If an IV has a significant effect on a DV, then the effect size must be large

Accepted Answer

The effect size depends on multiple factors such as the magnitude of the difference between groups, the variability of the data, and the sample size. A significant effect does not necessarily mean a large effect size.

Question 7

Cohen's d is an estimate of the true population mean.

Accepted Answer

Cohen's d is a measure of effect size that indicates the standardized difference between two means, not an estimate of the true population mean.

Question 8

Cohen's d can be used to estimate the size of a mean difference between two groups

Accepted Answer

Cohen's d is a measure of effect size that indicates the standardized difference between two means. It is often used to estimate the size of a mean difference between two groups.

Question 9

when using a two-way ANOVA, there are exactly 3 measurable partial -eta-squared.

Accepted Answer

A two-way ANOVA involves two independent variables, and the partial eta-squared can be calculated for each main effect and the interaction effect, totaling three.

Question 10

With respect to the value of Cohen's d, effects that are small are near 0, and effects that are considered large must be greater than 1.

Accepted Answer

Effects that are considered large are generally classified as having a Cohen's d greater than or equal to 0.8, not greater than 1. Small effects are typically classified as having a Cohen's d around 0.2 or below.

Question 11

A t-test found a significant effect in which one condition had a detrimental effect on the outcome, making it significantly worse. In this case, one would expect that Cohen's d would be negative.

Accepted Answer

Cohen's d measures the size of an effect, not its direction; it is concerned with the magnitude of the difference between two means, not whether the effect is positive or negative.

Question 12

Eta-squared cannot be greater than 1.00.

Accepted Answer

Eta-squared is always a value between 0 and 1, where 0 represents no effect and 1 represents a perfect relationship between variables. It is not possible for eta-squared to be greater than 1.00.

Question 13

eta-squared cannot be less than 0

Accepted Answer

Eta-squared is a measure of effect size that ranges from 0 to 1, with higher values indicating a stronger effect. It is calculated as the ratio of systematic variance to total variance. Since variance cannot be negative, eta-squared cannot be less than 0.

Question 14

Partial eta-squared are only used for factorial ANOVAs, and not one-way ANOVAs.

Accepted Answer

Partial eta-squared is a measure of effect size that is used to assess the proportion of variance in the dependent variable that is explained by the independent variable. It is typically used in factorial ANOVAs, where there are multiple independent variables, to assess the relative importance of each variable. In one-way ANOVAs, where there is only one independent variable, partial eta-squared is not used.

Question 15

Like an inferential test, confidence intervals can be calculated on mean differences to test for statistical significance..

Accepted Answer

Confidence intervals can be used to determine if the mean difference between two groups is statistically significant, and the results can be used to make inferences about the population as a whole.

Question 16

When the 95% confidence interval of the mean of one group does not include 0, it means that the difference between two groups is statistically significant at the .05 level

Accepted Answer

The statement about the 95% confidence interval not including 0 refers to the mean of a single group, not the difference between two groups. Statistical significance of a difference between two groups would be assessed by looking at the confidence interval of the difference between their means, not the mean of one group alone.

Question 17

Effect sizes and confidence intervals becoming less and less frequently used in published research. They are more common for oral presentations.

Accepted Answer

Effect sizes and confidence intervals are increasingly emphasized in published research for their importance in understanding the magnitude and precision of study findings, contrary to being less frequently used.

Question 18

One reason why effect sizes are generally omitted is because it makes the statistics harder to understand and does not provide any additional information that significance testing doesn't already tell us.

Accepted Answer

Effect sizes provide additional information beyond significance testing such as the practical significance or magnitude of an effect, and can aid in comparing results across studies. While understanding effect sizes may require additional statistical knowledge, they are a valuable component of statistical analysis. Therefore, the statement in the question is false.

Question 19

The 99% confidence interval of a mean difference is from .04 to 1.49. This suggests that the mean difference is significant at the alpha = .05 level.

Accepted Answer

A 99% confidence interval means there is a 99% probability that the true population mean falls within the given interval. Since the interval does not include zero (which represents no difference between the two means), we can conclude that the mean difference is statistically significant at the alpha = .05 level, as the confidence interval is entirely above zero. Therefore, we can choose option A as the correct answer.

Question 20

As confidence increases, precision generally decreases

Accepted Answer

As confidence increases, people tend to become less cautious and may overlook or ignore nuances or details, leading to a decrease in precision.

Quiz 12: Confidence Intervals and Effect Size: Building a Better Mousetrap