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.

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.

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.

The standard deviation is a measure of variability, or the spread of scores. The standard deviation is the average distance of the score from the mean. This makes sense when considering the formula, which involves subtracting the mean from each score, squaring it, calculating the sum, dividing by the number of scores, and taking the square root. The range is also a measure of variability, but simply involves subtracting the smallest score from the largest score. The mean, median, and mode are all measures of central tendency. The mean is the average of all scores. The median is the center score in the distribution. The mode is the score that occurs most frequently.

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.

A correlation coefficient is a statistical measure of the extent to which two factors vary together. Specifically, it measures how well one factor predicts another on a scale of -1 to 1, with numbers closer to -1 and 1 indicating the strongest relationships. Whether a correlation coefficient is negative or positive does not indicate the strength of the relationship, it simply tells us the direction of the relationship!

In order to calculate the mean (also called arithmetic average) we need to add all the values together and divide by the number of values. In this case, the total sum is 87 and the number of values is 12, so we perform the following calculation to obtain our mean:

To find the median, we simply arrange the values in order from least to greatest and identify the middlemost number. However, since 12 is an even number, there will not be one single middle value– therefore, we find the average of the two middlemost values. Arranged in ascending order, the data set is:

The 6th and 7th values are 6 and 7, so we know our median is the average of 6 and 7.

The mode is the value that occurs most frequently in the data set. Here, 6 occurs three times and no other number occurs more than twice, which means 6 is our mode.

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.

Statistical significance is a statistical criterion for rejecting the assumption of no difference between groups in a particular study. In order to establish statistical significance, researchers typically look for a p value of less than .05, which indicates that the study's findings were not obtained due to chance or error.

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.