# What is the difference between a dependent and independent relationship? How do you determine the measure of association, probability, condition, margin, independent variable, and dependent...

What is the difference between a dependent and independent relationship? How do you determine the measure of association, probability, condition, margin, independent variable, and dependent variable?

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An **independent variable** refers to what the experimenter is changing, while the **dependent variable** is what the experiment is measuring, or the variable that depends on the independent variable. **Conditions** are different values of the independent variable. For example, in many experiments there is an experimental condition and a control condition (in which the independent variable is not changed). For example, if you are testing a certain drug on a population, the experimental condition of the independent variable is giving the drug, while the control condition is not giving the drug.

In a table showing an independent versus dependent variable, the different conditions of the independent variable are shown along the left side. The different dependent variables are shown along the top, and each cell represents the dependent variable measurement for that condition. For example, in an experiment testing the results of a drug on the common cold, the conditions "no drug" and "drug" are listed along the lefthand side. On the top, there are results for whether the patients in the study have a cold ("YES") or don't have a cold ("NO"). The numbers in each cell represent the number of patients who have that condition. The **margins** (at the bottom of the 2x2 chart) are where the total numbers of patients in each condition and the total number of patients in the study go.

In a chart (or table), the **measure of association** between the dependent and independent variables is measured from -1 to 1. A value of zero means there is no relationship, while a measure of 1 means there is a perfect relationship. A level of -1 means that is a perfect negative relationship or correlation between the values; that is, as the independent variable goes up in value, the dependent variable goes down the same amount in value. You can tell the measure of association by looking at the cells in the independent versus dependent chart. For example, if more patients who receive a drug mark "NO," that they don't have a cold than those who who don't receive the drug, there is a measure of association between taking the drug and not having a cold.

In a **probability** distribution table, you can determine the probability that certain values of x (the independent variable) and y (the dependent variable) occur together. For example, if you find x on the horizontal axis of the table and y on the vertical axis, you find the cell where a certain value of x and a certain value of y meet. That will tell you the probability that those two values will occur together (see the link below for a graphic representation of a probability distribution table).