# Discrete Data vs. Continuous Data

Main DifferenceThe main difference between discrete data and continuous data is that discrete data is the countable data with some particular values that do have some spaces or intervals in between. On the other hand, continuous data is the kind of data that is measurable data, carries a continuous sequential pattern without intervals depicting streamflow. Discrete data carries finite values, whereas continuous data possess infinite values.

## Difference Between Discrete Data and Continuous Data

#### Discrete Data vs. Continuous Data

All the countable data falls in the category of discrete data whereas all the measurable data falls in the category of continuous data.

#### Discrete Data vs. Continuous Data

Discrete data is graphically represented using a bar while continuous data is graphically represented using a histogram.

#### Discrete Data vs. Continuous Data

Discrete data contains finite level of variance in particular values, on the other hand, continuous data contains infinite level of variance in sequential pattern of values.

#### Discrete Data vs. Continuous Data

Discrete data can take only particular values with certain spaces and intervals in between while continuous data can take any set of values in a certain amount of directed range.

#### Discrete Data vs. Continuous Data

Discrete data shows isolated points on the graph showing intervals or spaces whereas continuous data shows connected points on the graph depicting the continuous sequence of data.

#### Discrete Data vs. Continuous Data

Discrete data is mutually inclusive of all its attributes, on the flip side, continuous data is mutually exclusive of all its attributes.

#### Discrete Data vs. Continuous Data

Discrete data possess ungrouped frequency distribution while continuous data possess grouped frequency distribution.

#### Discrete Data vs. Continuous Data

Common examples of discrete data include simple countable data like days of the week, days of months, marks of a test, a scorecard of a cricket team, shoe size, etc. whereas common examples of continuous data include sequential measurements like temperature, humidity, resonance, viscosity, speed blood pressure, body measurements, length, weight, height, the price of product or service, etc.

## Comparison Chart

Discrete Data | Continuous Data |

Discrete data is a kind of quantitative data that can be counted. Or we can say that the kind of data that has spaces or intervals in between. | Continuous data is a kind of quantitative data that can be measured. Or in other words the type of data that carries a constant sequence without spaces. |

Functionality | |

Shows intervals or spaces. | Shows sequence of data. |

Representation | |

Bar graph | Histogram. |

Cataloging | |

Inclusive of all its attributes. | Exclusive of all its attributes. |

Tabulation | |

Ungrouped frequency mode. | Grouped frequency mode. |

Nature | |

Countable nature. | Measurable nature. |

Frequency | |

Ungrouped frequency distribution. | Grouped frequency distribution. |

Standards | |

distinct values | any value |

Common Examples | |

Days of the week, days of months, size of a shoe | Temperature, humidity, the price of product or service, height, weight, etc. |

### Discrete Data vs. Continuous Data

Discrete data contains a finite level of variance in the data points or intervals whereas contrary to this continuous data contains an infinite degree of variance in the sequential data patterns. Discrete data values being finite can even be predicted whereas, on the other hand, continuous data possess infinite values that cannot be predicted. Although continuous data do fall in a sequential range in a variety of particular data types but still as it is unlikely to be counted individually like discrete data, it lacks the specificity of discrete data.

### What is Discrete Data?

Discrete data is a kind of quantitative data that can be counted, or we can say that the kind of data that has spaces or intervals in between. 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 possess the countable nature and data can only take particular values, and that’s why they are tabulated in ungrouped frequency mode. The classification of discrete data is mutually inclusive of all its attributes. Graphically discrete data is represented on the bar graph usually. When represented on the graph, discrete data shows isolated points on the graph showing intervals or spaces.

#### Common Examples

Simple countable data like days of the week, days of months, marks of a test, a scorecard of a cricket team, shoe size, etc.

### What is Continuous Data?

Continuous data is a kind of quantitative data that can be measured. Or in other words the kind of data that carries a continuous sequence without spaces. Continuous data, unlike discrete data, can comprise of any value from the sequence with or without any interval. Contrary to discrete data, continuous data contains an infinite level of variance in the sequential data patterns. Continuous data possess the measurable nature and unlike discrete data continuous data can take any values from the sequential pattern and that why they are tabulated in grouped frequency mode. Continuous data is represented on the histogram and shows connected points on the graph depicting the continuous sequence of data. Contrary to discrete data, continuous data is mutually exclusive of all its attributes. Continuous data can take any set of values in a certain amount of directed range.

#### Common Examples

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

ConclusionAll kinds of alphanumeric or arithmetical data, that is way particular in nature and can be counted is referred to as discrete data for example days of weeks, shoe size, marks of a test, scorecard of a team etc. On the other hand all kinds of data that cannot be counted but can be measured falling in some range is categorized under continuous data for example temperature, product or service cost etc.