Nominal variables are categorical variables where the groups are related, but there is no natural ordering among them, for example, race, type of insurance, or geographical regions (North, South, East, West).

Ordinal variables are categorical variables where there is a natural order among the groups, such as ranking scales or letter grades. Differences are not precisely meaningful. For example, pain scale whose options are categorized as mild, moderate, and severe—if one patient scores her pain as mild and another as moderate, we cannot say precisely the difference in their scores like in continuous variables; however, it would tell us that moderate is greater than mild.

Binary variables are also called dichotomous variables. A variable is said to be binary when there are only two possible levels. Variables that can be phrased as a yes/no question are in this category. For example, if we were looking at gender, we would most probably categorize somebody as either “male” or “female.” This example of a binary variable is also a nominal variable. In another example, if education level of patients were categorized into less than high school and greater than high school, it would be a binary variable. Since there is some order to this classification, it is also an ordinal variable. Thus, when a categorical nominal or an ordinal variable has just two possibility outcomes, it can be labeled as a binary variable.

Mean —the arithmetic average of the observations in a sample. One needs to add up all the numbers and then divide by the number of numbers. This is usually used to describe normally distributed data.

Median—the middle observation, i.e., half of the observations are smaller and half are larger. To find the median, numbers have to be listed in numerical order from smallest to largest, so you may have to rewrite your list before you can find the median. Like in the question above, this is usually used in non-normally distributed data.

Mode—the observation that occurs most frequently in a sample. If no number in the list is repeated, then there is no mode for the list.

Range —the difference between the largest and the smallest observation. This can be used to describe any type of data, and is most useful when one is interested in knowing the most extreme (i.e., highest and lowest or largest and smallest) observations in a sample. The prime advantage of this measure of dispersion is that it is easy to calculate. On the other hand, it has lot of disadvantages. It is very sensitive to outliers and does not use all the observations in a data set.

Interquartile Range—the difference between the 25th and 75th percentiles; in other words the central 50% of all observations. This is most often used in non-normally distributed data, but can be used to describe the middle 50% of any sample. If the interquartile range is large, it means that the middle 50% of observations are spaced wide apart. The important advantage of interquartile range is that it can be used as a measure of variability if the extreme values are not being recorded exactly. The main disadvantage in using interquartile range as a measure of dispersion is that it is not amenable to mathematical manipulation.

Standard Deviation (SD) —it is the square root of sum of squared deviation from the mean divided by the number of observations. This is used in normally distributed data to describe the spread of the observations about the mean. In other words, it tells you how close the data are to the average value in the sample. The advantage of SD is that along with mean it can be used to detect skewness. The disadvantage of SD is that even though it can detect skewness, it is not an ideal measure of dispersion for skewed data.

Randomized controlled trials —subjects are randomly divided into groups. One group receives the intervention (patients and researchers maybe blinded to treatment) and followed forward in time. At the end of the study, the frequency of outcome is compared. This study design reduces the effect of unmeasured (confounding) variables that may influence outcomes of a study.

Non-randomized controlled trials —are similar to randomized controlled trials described above except that it specifically lacks the element of random assignment to treatment or control.

An observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints.

Cohort studies —subjects are divided into groups based on the presence or absence of an exposure over a period. The frequency of the outcome is compared. When these patients are followed over time, it is known as a prospective cohort study, whereas a retrospective cohort study (also called a historic cohort) is when a group of individuals who have shared a common exposure are compared to individuals who are not and their influence on the incidence of a condition such as disease or death.

Case control studies —subjects are divided into groups based on the presence or absence of the outcome of interest, and then the frequency of risk factors in each group is compared.

Cross section studies —in this type of study, the presence of the presumed risk factor and the presence of the outcome are measured at the same time in a population.

Case report/case series —the studies have a detailed report of signs, symptoms, diagnoses, treatment, and follow-ups; often individual or group of individuals.