Dr. Andrews’ study lacks external validity because his findings cannot be applied to the greater population. There is something about his sample—in this case, the fact that they all came from the United States and all were raised to speak English—that differs significantly from the greater population (i.e. not everyone in the world is a an English-speaking, American citizen); therefore, the results cannot be generalized to the entire population. A problem with internal validity would involve a problem with the proposed relationship between the measured variables (e.g. if Dr. Andrews had found that age and phoneme usage were completely unrelated or if Dr. Andrews discovered his methods for measuring phoneme acquisition were inaccurate). Anonymity is not mentioned in the description, but we can assume Dr. Andrews did not reveal any personally identifying information about his subjects; furthermore, anonymity would be unrelated to whether or not his results can be generalized to a larger group.

A test is reliable if it consistently measures what it is intended to measure. Reliability can be tested by giving the same test to the same group of people at two different points of time or giving two forms of a test to the same group of people at different points of time. Reliability is associated with the words "consistency" and "stability." All of the other choices are definitions of validity. A test has construct validity if it measures what it says that it measures. A test has content validity if it measures all aspects of a domain. A test has criterion validity if it can make a prediction based on test scores. 

The purpose of random sampling methods is to ensure that one individual does not have a greater chance of selection than any other. Although random sampling reduces the effects of random variables, like error, they are not completely eliminated. Representation is not guaranteed, but is likely heightened. Although randomness does not ensure conclusive results, it helps to validate any results of a study against one that is not random. 

Correlations range from  to , with  representing a perfect negative correlation and  representing a perfect positive correlation.

Statistically speaking, a correlation value of  indicates that if one variable is increasing, then the other variable is decreasing. Similarly, if one variable is likely to be present, then the other variable is unlikely to be present. There is a negative correlation between wearing a seatbelt and injury during a car accident.

Statistically speaking, a correlation value of  indicates that if one variable is increasing, then the other variable is also increasing. Similarly, if one variable is likely to be present, then the other variable is also likely to be present. There is a positive correlation between obesity and type II diabetes.

Statistically speaking, a correlation value of  indicates no relationship between the two variables.

Of the given answer choices, a correlation value of  gives the strongest evidence of correlation between the variables, even if the correlation is negative.

In an experiment, the independent variable is the predicted cause while the dependent variable is the predicted effect. The independent variable can be manipulated by experimentors to elicit a change in the dependent variable. The dependent variable cannot be directly manipulated.

For example, in a experiment that studies the effect of soda consumption on AP exam scores, the independent variable would be the amount of soda the subject drink (easily controlled by the experimentors) and the dependent variable would be AP exam scores (the unpredictable variable being tested).

Brenda is experiencing the placebo effect, in that she is reporting a positive effect (more energy) when she was given a blank pill. Brenda mentally prepared herself for the expected effects of the pill, and was thus able to mimic the expected outcome despite no actual biological change.

Mean is not a measure of variability - it is a measure of central tendency, along with mode and median. Measures of variability show the spread of a data set and describe how different points are from one another, whereas measures of central tendency are representative of the data set as a whole.

The Hawthorne effect is also known as "the observer effect." Subjects of a study will alter their behavior if they are aware they are being observed. Because the researcher was watching the experiment, it is possible that the subject behaved differently than he would have if he was not being watched.

Jessica's z-score indicates how many standard deviations below or above the mean Jessica's test score was. Jessica's test score of 70 was less than the mean (95), so we know that the the answer must be negative.

We know that the standard deviation was , so

Jessica's z-score is 

A PET scan measures chemical activity in the brain.

An EEG measures brain activity by detecting waves and is typically used in sleep studies.

A CAT scan uses x-rays to take images of the brain and can be useful when looking at the brain's physical structure.

An MRI also can be used to examine the structure of the brain, but uses magnetic resonance to produce images.