Difference Between Discrete Data and Continuous Data

Core difference

The main difference between discrete data and continuous data is that discrete data is the accounting data with some particular values ​​having some gaps or intervals between them. On the other hand, continuous data is the type of data that is measurable data, it carries a continuous sequential pattern with no gaps that represent flow rate. Discrete data has finite values, while continuous data has infinite values.

Discrete vs. Continuous Data

Discrete data contains a finite level of variation in data points or intervals, whereas, unlike continuous data, it contains an infinite degree of variation in sequential data patterns. Discrete data values ​​that are finite can even be predicted, while continuous data, on the other hand, possesses infinite values ​​that cannot be predicted. Although continuous data falls into a sequential range in a variety of particular data types, but still, since it is unlikely to be individually counted as discrete data, it lacks the specificity of discrete data.

Comparison chart

discrete data continuous data
Discrete data is a type of quantitative data that can be counted. Or we can say that the type of data that has spaces or intervals between them. Continuous data is a type of quantitative data that can be measured. Or in other words, the data type that carries a constant sequence with no spaces.
functionality
Show intervals or spaces. Shows the data stream.
Representation
Bar graphic Histogram.
Cataloging
It includes all of its attributes. Exclusive of all its attributes.
Tabulation
Ungrouped frequency mode. Grouped frequency mode.
Nature
accounting nature. measurable nature.
Frequency
Unclustered frequency distribution. Pooled frequency distribution.
Rules
distinct values some value
common examples
Days of the week, days of months, shoe size Temperature, humidity, price of the product or service, height, weight, etc.

What is discrete data?

Discrete data is a type of quantitative data that can be counted, or we can say that the type of data that has spaces or intervals between them. Discrete data can only consist of separate and distinct values ​​with spaces or some intervals. Discrete data contains a finite level of variance in the data points or intervals. Discrete data is countable in nature and the data can only take particular values, and that is why they are tabulated in ungrouped frequency mode. Discrete data classification is mutually inclusive of all its attributes. Generally, discrete data is graphically represented on the bar chart. When plotted on the graph, discrete data shows isolated points on the graph showing gaps or gaps.

common examples

Simple accounting data such as days of the week, days of months, test scores, a cricket team scorecard, shoe size, etc.

What is continuous data?

Continuous data is a type of quantitative data that can be measured. Or in other words, the type of data that carries a continuous sequence without spaces. Continuous data, unlike discrete data, can be part of any value in the sequence with or without interval. Unlike discrete data, continuous data contains an infinite level of variation in sequential data patterns. Continuous data has a measurable nature and, unlike discrete data, continuous data can take any value from the sequential pattern and is therefore tabulated in grouped frequency mode. Continuous data is plotted on the histogram and shows connected points on the graph representing the continuous stream of data. Unlike discrete data,

common examples

Sequential measurements such as temperature, humidity, resonance, viscosity, blood pressure velocity, body measurements, length, weight, height, price of the product or service, etc.

Key differences

  • All accounting data belongs to the category of discrete data, while all measurable data belongs to the category of continuous data.
  • Discrete data is graphed by a bar, while continuous data is graphed by a histogram.
  • Discrete data contains a finite level of variance in particular values, on the other hand, continuous data contains an infinite level of variance in a sequential pattern of values.
  • Discrete data can take on only particular values ​​with certain spaces and intervals in between, while continuous data can take on any set of values ​​in a certain amount of directed range.
  • Discrete data shows isolated points on the graph that show gaps or gaps, while continuous data shows connected points on the graph that represent the continuous stream of data.
  • Discrete data mutually include all of their attributes; on the other hand, continuous data is mutually exclusive of all its attributes.
  • Discrete data has an unclustered frequency distribution, while continuous data has a clustered frequency distribution.
  • Common examples of discrete data include simple accounting data such as days of the week, days of months, test grades, a cricket team scorecard, shoe size, etc., while common examples of continuous data include sequential measurements such as temperature, humidity, etc. resonance, viscosity, blood pressure velocity, body measurements, length, weight, height, price of the product or service, etc.

conclusion

All kinds of alphanumeric or arithmetic data, which are of a very particular nature and can be counted, are called discrete data, for example, days of the week, shoe size, test scores, team scorecard, etc. On the other hand, all kinds of the non-countable but measurable data that are in some range are classified as continuous data, for example, temperature, cost of product or service, etc.

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