{"id":779324,"date":"2023-02-13T11:00:00","date_gmt":"2023-02-13T11:00:00","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=779324"},"modified":"2026-11-12T10:42:24","modified_gmt":"2026-11-12T17:42:24","slug":"discrete-data-vs-continuous-data","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/discrete-data-vs-continuous-data\/","title":{"rendered":"Discrete Data vs Continuous Data: Main Differences and Examples"},"content":{"rendered":"\n

Discrete data vs continuous data comes down to one question: do you count it or measure it? Discrete data takes separate, countable values, such as the number of support tickets opened this week. Continuous data can take any value within a range, such as ticket resolution time, and only your measuring tool limits its precision.<\/p>\n\n\n\n

Both are types of quantitative data, and mixing them up has a cost. You end up with the wrong chart, the wrong summary statistic, and sometimes the wrong statistical test.<\/p>\n\n\n\n

In this guide, we break down the difference between discrete and continuous data with a side-by-side table, a four-step classification test, the gray areas that trip up analysts, and examples from customer, employee, and product research.<\/p>\n\n\n\n\n\n

Discrete data vs continuous data: key differences at a glance<\/strong><\/h2>\n\n\n\n

The core difference is that discrete data is counted in separate steps, while continuous data is measured on an unbroken scale. Everything else, from the chart you pick to the test you run, follows from that one distinction.<\/p>\n\n\n\n

\n \n \n \n \n \n \n \n \n \n \n \n \n \n
Feature<\/th>\n Discrete data<\/th>\n Continuous data<\/th>\n <\/tr>\n <\/thead>\n
How you get it<\/td>\n Counting<\/td>\n Measuring<\/td>\n <\/tr>\n
Possible values<\/td>\n Separate, countable values with gaps between them<\/td>\n Any value within a range, with no gaps<\/td>\n <\/tr>\n
Decimals<\/td>\n Usually whole numbers, or fixed steps such as half sizes<\/td>\n Any decimal, limited only by instrument precision<\/td>\n <\/tr>\n
Typical examples<\/td>\n Employees, purchases, survey completes, defects<\/td>\n Height, weight, time, temperature, revenue per user<\/td>\n <\/tr>\n
Frequency table<\/td>\n Each value gets its own row<\/td>\n Values are grouped into class intervals<\/td>\n <\/tr>\n
Class intervals<\/td>\n Non-overlapping, such as 10 to 19 and 20 to 29<\/td>\n Shared boundaries, such as 10 to under 20 and 20 to under 30<\/td>\n <\/tr>\n
Go-to graph<\/td>\n Bar chart, dot plot<\/td>\n Histogram, line chart, box plot<\/td>\n <\/tr>\n
Probability model<\/td>\n Binomial, Poisson<\/td>\n Normal, exponential<\/td>\n <\/tr>\n
Useful summaries<\/td>\n Counts, proportions, mode, median<\/td>\n Mean, standard deviation, percentiles<\/td>\n <\/tr>\n <\/tbody>\n <\/table>\n<\/div>\n\n\n\n

A quick sanity check helps when the table is not enough. Ask whether a value halfway between two possible answers makes sense. You cannot hire 12.5 people, but a project can take 12.5 days.<\/p>\n\n\n\n

What is discrete data?<\/strong><\/h2>\n\n\n\n

Discrete data is quantitative data that can only take specific, separate values, usually whole numbers that come from counting. Between any two possible values there is a gap, and nothing in that gap is a valid answer.<\/p>\n\n\n\n

A team can have 14 or 15 members, never 14.3. That is why people often call discrete data count data. Its values cannot be broken into smaller parts without losing their meaning.<\/p>\n\n\n\n

Discrete data is one of the two branches of quantitative data<\/a>. The set of possible values can be finite, such as the six faces of a die, or open-ended, such as the number of website visits in a day. Either way, you can list the values one by one.<\/p>\n\n\n\n

Not every discrete value is a whole number. Shoe sizes move in half steps, and US prices stop at the cent. What matters is that the values come in fixed, separate steps.<\/p>\n\n\n\n

Discrete data examples you will meet at work<\/h3>\n\n\n\n

Discrete data shows up wherever a team counts events, people, or items. Common discrete data examples include:<\/p>\n\n\n\n