Question 1

A researcher conducts an experiment on a new drug for enhancing memory and the researcher concludes that the drug is effective. However, by chance alone the many participants having excellent memories were placed in the experimental group. This may lead to a:

Accepted Answer

A Type I error occurs when a researcher incorrectly rejects a true null hypothesis, which in this case would mean concluding the drug is effective when it might not be, due to the biased sample.

Question 2

If we reject the null hypothesis when it is true, we make a:

Accepted Answer

Rejecting the null hypothesis when it is actually true leads to a Type I error, also known as a "false positive". This means that we incorrectly conclude that there is a significant effect or difference when there really isn't one.

Question 3

Kim has conducted a study and based on her statistical analysis of the results has incorrectly rejected the null hypothesis. Kim is committing a:

Accepted Answer

Kim has rejected the null hypothesis incorrectly, which means that she has incorrectly concluded that there is a significant relationship between the variables being studied, when in fact there is not. This is a Type I error, which is the rejection of a true null hypothesis.

Question 4

____ can be determined by calculating the difference between the results obtained in the experimental and control groups.

Accepted Answer

Effect size is a measure of the magnitude of the difference between the means of the experimental and control groups. It is calculated by dividing the difference between the means by the standard deviation of either group. It provides an estimate of the practical significance of the results, beyond statistical significance, and can be used to compare the effectiveness of different interventions or to determine the sample size needed for future studies. Between-groups variance (A) and within-groups variance (B) are components of variance used in ANOVA to determine the main effects and interactions between variables. Power (D) is the probability of detecting a true effect, given a specific sample size and level of significance.

Question 5

If we fail to reject the null hypothesis when it is true, we make a:

Accepted Answer

When we fail to reject the null hypothesis when it is true, we are making a correct acceptance. A Type I error occurs when we reject the null hypothesis when it is actually true. A Type II error occurs when we fail to reject the null hypothesis when it is actually false.

Question 6

The magnitude of the change that the independent variable has on the dependent variable is called:

Accepted Answer

The effect size refers to the magnitude of the change that the independent variable has on the dependent variable. The between-groups and within-groups variances are measures of variability in the data, while power refers to the ability of a study to detect a statistically significant effect.

Question 7

The power of a statistical test is the probability of:

Accepted Answer

The power of a statistical test is the probability of correctly rejecting a false null hypothesis. It is denoted by 1-β (beta) where β is the probability of a Type II error, i.e., failing to reject a false null hypothesis. Option D is the only one that mentions β in the formula, hence it is the correct answer.

Question 8

The probability of rejecting the null hypothesis when it is true is called:

Accepted Answer

Alpha (α) is the probability of making a Type I error, which occurs when the null hypothesis is rejected when it is actually true.

Question 9

If we retain the null hypothesis when it is true, we make a:

Accepted Answer

The answer of If we retain the null hypothesis when...

Question 10

If we fail to reject the null hypothesis when it is false, we make a:

Accepted Answer

Failing to reject the null hypothesis when it is actually false is defined as a Type II error.

Question 11

The larger the F ratio:

Accepted Answer

A larger F ratio indicates a larger difference between the groups being compared, which makes it less likely that our results are due to chance.

Question 12

We may reject the null hypothesis when the value of F:

Accepted Answer

When one-way ANOVA is performed, the F-statistic is used to determine whether there is a significant difference between the means of the groups being compared. F-value is calculated by dividing the variance between the groups by the variance within the groups. When the null hypothesis is rejected, it means that there is a significant difference between at least one of the groups being compared. In this case, the F-value should be greater than 1. Therefore, the correct choice is C (>1). Choices A and B are incorrect because an F-value of 0 or 1 indicates that there is no difference between the groups being compared. The choice D is incorrect because an F-value less than 1 indicates that the variance within the groups is greater than the variance between the groups, which means that there is no significant difference between the means of the groups.

Question 13

A large F can be produced by:

Accepted Answer

A large F-value indicates a significant difference between the means of two or more groups. This can be achieved by increasing the variability between groups (i.e., increasing between-group variance), reducing the variability within groups (i.e., reducing within-group variance), or both. Therefore, the best choice is D (increasing between-group variance, reducing within-group variance, or both).

Question 14

If within-groups variance is large relative to between-groups variance, then there is increased probability of:

Accepted Answer

When within-groups variance is large relative to between-groups variance, it indicates that the differences within each group are greater than the differences between the groups. This scenario makes it harder to detect a true effect or difference between groups, thereby increasing the probability of making a Type II error, which occurs when the null hypothesis is falsely accepted (failing to reject the null hypothesis when it is false).

Question 15

In order to increase the probability of rejecting the null hypothesis, we should ____ treatment variance and ____ error variance.

Accepted Answer

In order to increase the probability of rejecting the null hypothesis, we want to maximize the treatment variance (the variability of the treatment effect) and minimize the error variance (the amount of variability that is not explained by the treatment effect). By doing so, any observed differences between groups are more likely to be due to the treatment rather than chance. Therefore, we should choose option B, which maximizes treatment variance and minimizes error variance.

Question 16

The probability of rejecting the null hypothesis when it is false is called:

Accepted Answer

The probability of rejecting the null hypothesis when it is false is called the power of the test, which is mathematically represented as 1 minus the probability of Type II error (beta).

Question 17

The F ratio tells us how likely it is that :

Accepted Answer

The F ratio provides information about the variability between groups and the variability within groups. By comparing these two sources of variability, we can determine if our results are statistically significant (i.e., not likely to have occurred by chance). Therefore, the F ratio tells us how likely it is that our results occurred by chance, but it does not provide information about causality or the presence of confounds.

Question 18

Extraneous variables that affect groups equally:

Accepted Answer

Extraneous variables that affect groups equally add overall variability to the data, but do not introduce systematic bias or affect rejection of the null hypothesis. These variables are not considered confounds because they do not selectively impact one group over another.

Question 19

The probability of making a Type II error is called:

Accepted Answer

The probability of making a Type II error is called beta.

Question 20

Alpha is symbolized by ____ and tells us about the probability of making a ____ error.

Accepted Answer

Alpha is symbolized by the Greek letter alpha (α) and represents the probability of making a Type I error, which is the rejection of a true null hypothesis. Choice A is incorrect because it includes the symbol for population standard deviation (σ) instead of alpha. Choice B is incorrect because it includes the symbol for chi-squared (χ) instead of alpha. Choice D is incorrect because it includes the symbol for population mean (μ) instead of alpha.

Quiz 7: Behavioral and Social Learning Theories