A Pearson correlation between two variables indicates that as one variable increases, another variable either increases or decreases by some amount in a linear manner.

Correlations range between  and , with  signifying the strongest possible negative correlation between two variables,  signifying the strongest possible positive correlation, and  signifying the complete absence of any correlation between two variables.

An illusory correlation occurs when someone thinks that two variables are related, but they are actually not (they have a correlation of 0). 

Correlation coefficients range from -1 to 1, with the strongest correlations being closer to -1 or 1. A correlation of 0 indicates no relationship between two variables. Negative correlations can be as strong or stronger than positive correlations; the most important factor is the magnitude of the correlation.

The median is the middle number in a data set. For example, the median of {3, 7, 4, 5, 8} is 5. The terms must first be placed in increasing order: {3, 4, 5, 7, 8}. From here, it becomes evident that 5 is the middle term, or median.

Statistics play a big role in much of the research in the field of psychology. In studying groups of people and individuals, their behaviors, reactions, etc. a researcher will have lots of numerical data on their hands. Statistics is the analysis of this numerical data.

A study's validity is its ability to scientifically measure the concepts it was designed to measure. A study's reliability is its ability to yield similar results when replicated in a similar setting.

Because Carol's study could be replicated but does not answer the question it was designed to answer her study is reliable, but not valid.

The number describing two variables relationship is the correlation coefficient. This could show a positive correlation (+ number) or a negative correlation (- number). The further from 0 the coefficent is the stronger the correlation. 

The mean is the average of a data set. On the other hand, the median is considered to be the 50th percentile—that is cutting the entire data set in half and representing the exact middle of the data set. The mean may be skewed depending on the data set. For instance, the median may be 50 but because of a few higher numbers, the mean could be 56. Using the mean as a measure of central tendency may cause the distribution to be a bit lopsided. Percent error is a statistical measure of the marginal error of actual results compared to expected results; therefore, percent error would not be the correct answer. Additionally, standard deviation is the measurement of the deviation from a group as a whole. This calculation is often conducted using averages (means); thus, the options standard deviation and standard deviation of the mode would also be incorrect. 

Type II error—also called a false negative—occurs when a researcher incorrectly fails to reject a false null hypothesis. In other words, the researcher should have rejected the null hypothesis in favor of the experimental hypothesis. In this case, the researcher should have found evidence in favor of the experimental hypothesis that ginseng consumption has a significant effect on memory; however, the researcher failed to do so because of some experimental error (i.e. random or systematic, the problem does not specify).