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.

Validity and reliability are both important terms for experimental design and are both markers of a good study. A study is valid if it measures what it is supposed to measure and reliable if it produces similar results every time it is conducted. If it is not valid, then it cannot answer the hypothesis or be applied to the real world; furthermore, if it is not reliable, then the results cannot be trusted.

A study is valid if it measures what it is supposed to measure and reliable if it produces similar results every time. The choice that stated “a scale that shows you that you are 50 pounds every time you step on it (i.e. reliable), but you know that you are really 150 pounds (i.e. not valid)” is reliable but not valid.

A study is valid if it measures what it is supposed to measure and reliable if it produces similar results every time. The choice that stated “a scale that shows you between 140-160 pounds every time you step on it (not reliable) and you know you weigh 150 pounds (valid)” is valid but not very reliable.