What is the difference between continuous and discrete data

So, discrete data refers to the type of quantitative data that relies on counts. It contains only finite values, whose subdivision is not possible. It includes only those values that can only be counted in whole numbers or integers and are separate which means the data cannot be broken down into fraction or decimal.

For example, Number of students in the school, the number of cars in the parking lot, the number of computers in a computer lab, the number of animals in a zoo, etc. Continuous data is described as an unbroken set of observations; that can be measured on a scale.

It can take any numeric value, within a finite or infinite range of possible value. Statistically, range refers to the difference between highest and lowest observation. The continuous data can be broken down into fractions and decimal, i.

For Example, Age, height or weight of a person, time taken to complete a task, temperature, time, money, etc. The difference between discrete and continuous data can be drawn clearly on the following grounds:.

Hence, with the above explanation and example, it would be quite clear that the two types of data are different. Discrete data expects a certain number of isolated values. In contrast to continuous data, which expects any value from a given range without any breaks , and is related to physical measurement.

Very good and well explained concepts. Discrete data contains distinct or separate values. Continuous data includes any value within the preferred range. Both discrete and continuous data are valuable for all sorts of data-driven decisions. Valuable research and insights are made by combining both sets of data. Here are some examples where discrete and continuous data can be used:.

Marketing and advertising. Before engaging in any marketing or advertising campaign, companies need to analyze internal and external factors that may affect the marketing campaigns. In most cases, marketing professionals are using SWOT analysis. The primary objective of this analysis is to help companies develop a full awareness of all the factors involved in making data-driven decisions.

Numerical types of data are popular among researchers due to their compatibility with most statistical techniques. Discrete and continuous data helps to ease the research process. Population analysis. Using trends analysis, researchers gather the data on various rates in a country or a region for a certain period and predict future populations.

This might include birth, death rates, languages popularity, et cetera. Predicting a country's demographics plays a vital role in economics. Product development. Product researchers use total unduplicated reach and frequency analysis TURF to investigate if a new product or service will have the demand and will be well-received in the target market during the product development stage. However, the implementation of discrete or continuous data might not always provide accurate results, as there are challenges related to only analyzing numerical data.

Discrete or continuous data research can be limited in their pursuit of statistical relationships. It can lead to researchers overlooking valuable insights. By focusing solely on numbers, analyst runs into the risk of missing big-picture information that can benefit the business.

When conducting research, analysts need to develop a hypothesis and set up a model for collecting and analyzing data. Sometimes even coming up with a hypothesis can be subjective, especially if there is a specific question that needs to be answered and proved by not only numerical data. Whatagraph can come in handy and ease the labor-intensive process of data collection and aggregation.

The reporting tool automatically gathers data from different sources and presents it in a visual report. The collected data can be shown in various charts and graphs, including pie charts for discrete data and line graphs for continuous data. Discrete data presents a certain number of isolated values. In contrast — continuous data shows any value from a given range. Understanding the numerical data and the difference between discrete and continuous data might pose a challenge initially.

The best answers are voted up and rise to the top. Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. What is the difference between discrete data and continuous data? Ask Question. Asked 11 years, 3 months ago. Active 1 year, 9 months ago. Viewed 1. Improve this question. Jeromy Anglim Albort Albort 1 1 gold badge 9 9 silver badges 10 10 bronze badges. For me, it gives this. Same thing - different names. For some reasons, intro stat classes seem to really enjoy making students memorize rules to distinguish these two things.

As far as I've been able to understand, the differences are not in the data--but in how we choose to model the data. Show 1 more comment. Active Oldest Votes. It sometimes makes sense to treat discrete data as continuous and the other way around : For example, something like height is continuous, but often we don't really care too much about tiny differences and instead group heights into a number of discrete bins -- i.

It seldom makes sense to consider categorical data as continuous. Improve this answer. Xanlantos 3 3 bronze badges. Some very interesting further points were made in response to this follow-up question. They don't come up all that often in practice, but it's perfectly possible for them to come up for real; indeed I can think of two distinct if related examples that can easily arise.

Add a comment. See this quote All actual sample spaces are discrete, and all observable random variables have discrete distributions. References Pitman, E. Some basic theory for statistical inference. London: Chapman and Hall.

Jeromy Anglim Jeromy Anglim Overall, this interesting reply seems based on an untenable premise that data should be characterized by the values they do have rather than by the values a mathematical model allows them to have.

The latter is the crucial characteristic, not the former. Web versions are available through a Google scholar search. Lord is addressing a misconception, hotly debated 60 years ago, about the extent to which "measurement theory" should influence or even limit the scope of statistical analysis. My point was a different one about the distinction between model constructs and observations. It can be 23 degrees, Something you could represent with a whole number like 1, 2, etc The difference is important as many statistical and data mining algorithms can handle one type but not the other.

Also, in "regular" OLS? What these points and counterpoints begin to suggest is that data are not necessarily discrete or continuous, but rather statistical procedures are discrete or continuous.

Example: the number of students in a class you can't have half a student.