Often, when reading a statistics book, you will see some variation on the phrase “**independent data**“. Many models assume that the data are independent. Sometimes this is abbreviated as part of the acronym iid which means independent and identically distributed.

You may get confused between this and the case of **independent and dependent variables**, which I discussed here. But the two ideas are quite different.

When we say **data** are independent, we mean that the data for different subjects do not depend on each other. When we say a **variable** is independent we mean that it does not depend on another variable for the same subject.

For instance, if we are trying to predict the weight of adult humans, we might gather a sample of adults, and collect various bits of information – height, weight, sex, age, and perhaps many others. Weight is a **dependent variable** because it depends on the other variables – taller people tend to be heavier; men tend to be heavier than women, and so on. But the ** data are independent if the weight and other variables for one person aren’t related to those for another.**

**Sometimes, though, the data are dependent . One example is if we measured some variables on a bunch of children, but chose kids who were in particular classes in particular schools: Kids in a class are likely to be more similar to each other than kids in different classes. **

**Another example is when we measure the same person (or other subject) more than once. If I give a bunch of students a midterm and a final, their final grade is likely to depend on their midterm grade, not just because of a general relationship between the two grades, but because it is the same person.**

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So for example, I am doing a study at school and I am looking at information from a leadership survey and an employee engagement survey. I want to know how the employee ranks their supervisor on the leadership survey and separately how they rank themselves on the employee engagement survey. Then I want to see if there is a relationship (correlation). I wanted to use Kendall’s Tau because I understand it can report the strength of the relationship and handle independent ordinal data.

Am I thinking about this correctly????

Sounds right to me!

Many thanks ^_^

Hello, can you explain independent data in example with plants. For examlple you have 2 fields, each field devided into 3 sections 2 control and 1 gmotreetment. from each section we ll take random samples from different spots in different time. In my opinion the data points within section is dependent because they are living in the same conditions, but if i will take 10 spots in 1 section, and from each spot ll take 1 individual and measure it. If these data will be independent between these 10 spots or not??? and i understand that all these 3 sections have depenent data, because they are growing it the same conditions, but if i ll separate them can these data be independent?

I am not an expert on plants, by any means, but it seems to me that data from a single section will be independent if considered alone – then it would be as if the other sections and fields did not exist. But if you are considering multiple sections, then data within one section is dependent.