An achievement test is a test that is designed to assess a person's current skill set. An achievement test often contrasts with an aptitude test, which has more to do with a person's consistent traits that will predict their future success in particular areas. Achievement tests are more often administered in schools to test a student's given mastery of particular material. This very quiz is an example of an achievement test.

"Norms," with regard to research and testing, refers to the percentile ranks of a large and representative sample of a particular demographic for which the test was designed. This is not to be confused with societal norms, which have to do with the rules people adhere to in society.  

A standardized score reflects a person's status relative to the average of all scores on the given evaluation. That is, the standard score reflects a person's relationship with the mean of all scores. If the average of all scores is 70, and a person scores 90, the person's standardized score is positive 20. If the person scores 20 below the mean, their standardized score would be negative 20.  

Someone who is high in intrapersonal intelligence fuctions at their highest level while alone. This can often translate into high-self esteem, since they provide for their own individual needs instead of relying on other people for support. They process their lives during their alone time, reflecting on succcesses and mistakes. This contrasts with people who have high interpersonal intelligence, who may get these needs met from communicating with other people.

When a researcher tries to take measurements, they are doing so in the most precise and controlled manner possible. However, there will be some moments when this precision and control is not always possible, and a chance variation may occur. The researcher will always try and reduce the amount of chance variations to the extent that they can.

Descriptive statistics are primarily concerned with numbers. Its generalizations are primarily mathematical. Inferential statistics, however, are more concerned with the generalizations that can be made from a given data set. One must be sure, though, that the data set is a represenative sample of a given population. Inferential statistics allows for researchers to make statements such as "50-year-old males prefer to eat hamburgers for breakfast" given their numerical data.

In a numerical data sheet, numbers can vary greatly. The measure of how much variation (or dispersion) there is is called the measure of variation.

A valid test will measure what it is intended to measure. For instance, a test designed to help diagnose depression must ask questions that pertain to depressive disorders and offer answer options that are relevant to attaining a diagnosis. If a test designed to diagnose depressions accurately assesses patients, then it is said to have high validity. If a test designed to diagnose depressive disorders instead presents positive results for a random sample of individuals, then it must have low validity; it fails to perform its designed function.

External validity looks at the extent to which a study's findings can be generalized to certain populations. Studies that have large sample sizes and diverse participants have greater external validity.

The Pearson correlational coefficient (r) is a value between -1.00 and +1.00 that represents the degree of linear correlation between two variables, that is, the degree to which two variables are consistently either directly (for positive values) or inversely (for negative values) proportional to each other. Although a Pearson correlational coefficient of r=0.00 for Variables A and B means there is no linear correlation between them, other kinds of correlation in which the rate does not remain constant (e.g. a curvilinear correlation) are still possible.