## Key Points

- Continuous data is numerical data with infinite possibilities, depending on the range given.
- Attribute data is qualitative data, nominally used for ordinal purposes.
- Both data types are endlessly useful for statistical analysis purposes.

## What is Continuous?

Continuous data refers to numerical data with any value within a certain range. The values have infinite possibilities, but they all fall within a range. These can be whole numbers or decimals measured using data analysis like skews and line graphs. This kind of data can change over time and have different values during different time intervals.

An example of continuous data would be the weight of livestock. This could be expressed in infinite values such as 14.52 pounds, 1.56 pounds, and so on.

### The Benefits of Continuous

Continuous data is highly accurate as it can utilize decimals and can include infinite values. It is also ideal for displaying variations. A major benefit of continuous data is that there is a large variety of options for how to display it, everything from histograms to line graphs. It is also not necessary to have a great deal of continuous data to successfully implement graphical analysis or statistical tests.

### How to Present Continuous Data

Histograms are a standard way to present continuous data as they can display the value distribution. Dot plots are also common as they can show the same type of data as a histogram. If you have two continuous variables, a scatterplot is ideal and correlation can be utilized to assess relationship strength. Implement regression analysis in this instance to find the equation that creates the line of best fit for the data. Should you have continuous data divided into groups, a boxplot can display each group’s central tendency and spread.

## What is Attribute Data?

Attribute data is a kind of data considered qualitative as well as classifiable and countable. This kind of data can be further broken down into ordinal and nominal data. Ordinal refers to data that has a logical sequence to it, while nominal data does not.

An example of attribute data would be if a car is defective or not. If the question is whether a car is defective or not, the answer is a simple “yes” or “no”. It will not be broken down into decimals or fractions like 0.25, 1.12, and so on.

### The Benefits of Attribute Data

Attribute data is very easy to collect. It is also useful in being able to easily communicate complicated concepts into easily manageable things.

Further, while it is qualitative on the surface, it can be readily turned into numerical data. However, you need to keep in mind the limitations of using such data for calculations and other operations. You can’t have half of the color red, just as an example.

### How to Present Attribute Data

The best way to present attribute data is with a control chart known as an attribute chart. There are four types of attribute charts. These are n charts, c charts, np charts, and u charts. Which type of chart you use will depend on whether you have an issue with defects or defective products and if you have a fixed or variable sample size. For example, an np chart is used with attribute data that is collected in subgroups that are all uniform in size.

## Continuous vs. Attribute: What’s the Difference?

Continuous data tends to be much more detailed than attribute data, as attribute data comes into play when standard forms of measurement are difficult to collect. Attribute data can only be grouped into different categories, while continuous data can have an infinite number of values.

## Continuous vs. Attribute: Who Would Use A and/or B?

You could use attribute data when you are looking at the qualities of data that are simply a “yes” or “no” or cannot be divided further. For example, you could look at a group of people and determine if they are pregnant or not. No one is going to be half or a quarter pregnant. So you can consider it attribute data when the value will be finite and likely whole.

Alternatively, you can count it as continuous data if you look at the group of pregnant individuals and break down in decimal form how far along they are in their pregnancy, from 0–9 months. So if someone is six weeks pregnant, this could be expressed as 1.5 months pregnant and could be considered continuous data.

## Choosing Between Continuous and Attribute: Real-World Scenarios

A manufacturing plant wants to look at the number of its employees who can accomplish a given task within an eight-hour working day. It breaks down whether an employee completed a task or not and uses an attribute chart to display this information. It then takes the employees that did finish the task and looks at the amount of time it took each employee. This information is broken down into decimals, and this continuous data is presented with a histogram.

## Other Useful Tools and Concepts

Looking for some other ways to bolster your organization? Learning how to implement AHP in your decision-making process can lead to faster and more informed decisions. This approach uses data to drive your team forward toward making a decision.

Additionally, if you’re looking for additional statistical tools, you might consider the likes of the Bartlett Test. This is similar to other 1-way ANOVA tests but has a few distinct differences to make it worth considering.

## Summary/Conclusion

Having an understanding of both types of data and how they are best used is extremely important. It is also necessary to know that these two types of data are not used interchangeably with one another. The same tools are not used to present both.

It is worth noting that attribute data can be incorporated into continuous data, but the nature of continuous data does not allow for it to be incorporated into attribute data.