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).

2.3 percent of individuals fall two standard deviations above the mean in a normal distribution. 2.3% of 1000 individuals is 23 individuals that fall above this cutoff. 

Convert the percentage into a ratio.

Create a ratio using the total number of the sample size. Use the variable  as a representation of the number of individuals that fall two standard deviations above the mean.

Create a proportion using the two ratios.

Cross multiply and solve for .

Simplify.

Divide both sides of the equation by .

Solve.

Any correlation between .30 and .69 is considered moderate in strength. Anything above .60 is considered strong. Anything below .30 is considered weak or no correlation.  

A type I error occurs when a null hypothesis is rejected when researchers should have failed to reject the null hypothesis, in other words, when the researchers reject a true hypothesis. Remember, a null hypothesis is the assumption that there is no difference between the groups participating in the study. So if a type I error occurs, that means that the researchers are stating there is a difference between the groups when there really is not a difference. A Type II error involves the acceptance of a false hypothesis.

A type II error occurs when researchers fail to reject their null hypothesis when they should have rejected it. A null hypothesis is the assumption that there is no difference between the groups being studied. So if a type II error occurs, that means that the researchers are stating there is not a difference between the groups when there really is one.

Central tendency measures include the mean, median, and mode. In this case, the median would be best because several outliers skew this data set (e.g. perfect scores). The mean would be influenced strongly by these outliers. 

A correlation coefficient is a measure of the relationship between two variables. Correlation coefficients range from -1 to +1. For this reason -1.5 is incorrect because it is not possible to have a correlation coefficient less than -1. This question is asking for a coefficient with a strong inverse relationship. A positive number indicates a direct relationship, while a negative number indicates an indirect or inverse relationship. The specific number indicates the strength of the relationship. If the correlation coefficient is closer to -1 or +1, then it is said to be strong. As the number moves closer to zero, the strength of the relationship decreases; therefore, -0.90 is the best answer.