"Data! Data! Data! i can"t make bricks there is no clay."— Sherlock Holmes, in Arthur Conan Doyle"s *The Adventure the the Copper Beeches*

Whether you"re the world"s biggest detective do the efforts to cracked a instance or a human trying to solve a problem at work, you"re going to need information. Facts. *Data*, together Sherlock Holmes says.

You are watching: Quantitative data can be further classified as continuous or nonsequential.

But not all data is created equal, particularly if you plan to analyze as part of a quality development project.

If you"re making use of chathamtownfc.net statistical Software, you can accessibility the Assistant to guide you with your analysis step-by-step, and aid identify the type of data girlfriend have.

But it"s still necessary to have at least a straightforward understanding the the different species of data, and the kinds of inquiries you deserve to use them come answer.

In this post, I"ll administer a basic overview that the species of data you"re most likely to encounter, and we"ll usage a box of my favorite candy—Jujubes—to illustrate just how we have the right to gather these various kinds that data, and what types of evaluation we can use that for.

## The Two main Flavors the Data: Qualitative and Quantitative

At the highest level, 2 kinds of data exist: * quantitative*and

*.*

**qualitative**** Quantitative **data faces numbers and also things you deserve to measure objectively: size such as height, width, and length. Temperature and also humidity. Prices. Area and also volume.

** Qualitative **data encounters characteristics and also descriptors the can"t be easily measured, but can be observed subjectively—such as smells, tastes, textures, attractiveness, and color.

Broadly speaking, as soon as you measure something and also give it a number value, you create quantitative data. When you classify or referee something, you produce qualitative data. For this reason far, for this reason good. But this is simply the highest level of data: there are likewise different species of quantitative and qualitative data.

## Quantitative Flavors: consistent Data and also Discrete Data

There are two varieties of quantitative data, i m sorry is additionally referred to as numeric data: * continuous *and

*As a basic rule,*

**discrete**.*counts*are discrete and

*measurements*are continuous.

** Discrete **data is a count that can"t it is in made much more precise. Frequently it involves integers. Because that instance, the variety of children (or adults, or pets) in your family is discrete data, because you room counting whole, indivisible entities: you can"t have actually 2.5 kids, or 1.3 pets.

** Continuous **data, ~ above the other hand, could be divided and reduced come finer and finer levels. For example, you have the right to measure the elevation of your children at progressively more precise scales—meters, centimeters, millimeters, and also beyond—so height is constant data.

If i tallythe variety of individual Jujubes in a box, that number is a item of discrete data.

If I use a scale to measure up the weight of each Jujube, or the load of the whole box, that"s continuous data.

Continuous data deserve to be supplied in numerous different kinds of theory tests. Because that example, to assess the accuracy that the weight printed on the Jujubes box, we could measure 30 boxes and also perform a 1-sample t-test.

Some analyses use constant and discrete quantitative data at the same time. For instance, we could perform a regression analysis to watch if the weight of Jujube boxes (continuous data) is correlated with the variety of Jujubes within (discrete data).

## Qualitative Flavors: Binomial Data, in the name Data, and Ordinal Data

When friend classify or categorize something, you develop *Qualitative* or attributedata. There space three main kinds that qualitative data.

* Binary*data place things in among two mutually exclusive categories: right/wrong, true/false, or accept/reject.

Occasionally, I"ll acquire a crate of Jujubes that contains a couple of individual pieces that room either too hard or too dry. If i went with the box and also classified each piece as "Good" or "Bad," that would be binary data. I might use this sort of data to build a statistical design to predict how typically I deserve to expect to get a bad Jujube.

When collecting * unordered *or

*data, we assign individual items to called categories that do not have an latent or organic value or rank. If ns went with a crate of Jujubes and also recorded the shade of each in mine worksheet, that would certainly be in the name of data.*

**nominal**This type of data have the right to be used in plenty of different ways—for instance, I might use chi-square evaluation to check out if there are statistically significant differences in the quantities of each shade in a box.

We also can have actually ** ordered **or

*data, in i beg your pardon items space assigned come categories that do have actually some type of implicit or herbal order, such as "Short, Medium, or Tall." one more example is a survey inquiry that asks us to rate an object on a 1 come 10 scale, with 10 gift the best. This implies that 10 is far better than 9, i beg your pardon is much better than 8, and so on.*

**ordinal**The uses for notified data is a issue of some debate amongst statisticians. Everyone agrees its ideal for producing bar charts, but beyond that the answer to the question "What have to I execute with my ordinal data?" is "It depends." Here"s a post from one more chathamtownfc.net that offers terrific summary the the considerations involved.

## Additional Resources about Data and Distributions

For more fun statistics you have the right to do through candy, examine out this write-up (PDF format): statistical Concepts: What M&M"s can Teach Us.

See more: What Is The Formula For Nickel Ii Iodide Powder, Nickel(Ii) Iodide Powder

For a deeper exploration of the probability distribution that use to different types of data, check out my partner Jim Frost"s posts around understanding and also using discrete distributions and how to recognize the distribution of your data.