What is the Difference Between Discrete and Continuous Distributions?
🆚 Go to Comparative Table 🆚The main difference between discrete and continuous probability distributions lies in the nature of the values that the random variables can take on.
Discrete Distributions:
- Discrete distributions are probability distributions where the random variable can only take on a finite or countable number of values.
- Discrete variables can only assume one of a countable (usually finite) number of values.
- For example, the number of heads in a coin flip or the number of clicks on a webpage in an hour can be considered discrete variables.
- Discrete probability distributions can be presented in tabular form, with each possible value of the discrete random variable associated with a non-zero probability.
Continuous Distributions:
- Continuous distributions are probability distributions where the random variable can take on any value within a certain range or interval.
- Continuous variables can assume one of an infinite (uncountable) number of values.
- Examples of continuous variables include height, weight, or temperature.
- Continuous distributions are described in terms of probability density, which can be converted into the probability that a value will fall within a certain range.
In summary, discrete distributions involve random variables with a limited set of possible values, while continuous distributions involve random variables with an infinite number of possible values within a given range.
On this pageWhat is the Difference Between Discrete and Continuous Distributions? Comparative Table: Discrete vs Continuous Distributions
Comparative Table: Discrete vs Continuous Distributions
Here is a table comparing the differences between discrete and continuous distributions:
| Feature | Discrete Distributions | Continuous Distributions |
|---|---|---|
| Definition | A discrete distribution is one in which data can only take on certain values, typically integers. | A continuous distribution is one in which data can take on any value within a specified range. |
| Values | Discrete data can only take on specific, countable values. | Continuous data can take on any value within a specified range, making it quantifiable rather than countable. |
| Probability | In a discrete distribution, each possible value can be associated with a non-zero probability. | The probability that a continuous random variable will assume a particular value is zero. |
| Probability Distribution | Discrete probability distributions describe probabilities associated with discrete variables. | Continuous probability distributions describe probabilities associated with continuous variables. |
| Examples | Examples of discrete distributions include the number of children in a household, the number of clicks on a webpage in an hour, and the number of items sold in a day. | Examples of continuous distributions include heights of people, weights of objects, and temperatures of a room. |
In summary, discrete distributions have specific, countable values with non-zero probabilities, while continuous distributions have values that can take on any value within a specified range and have a probability of zero for any particular value.
Read more:
- Discrete vs Continuous Probability Distributions
- Discrete vs Continuous Data
- Discrete vs Continuous Variables
- Discrete Function vs Continuous Function
- Continuous vs Discontinuous Variation
- Continuous vs Discrete Spectrum
- Random Variables vs Probability Distribution
- Gaussian vs Normal Distribution
- Poisson Distribution vs Normal Distribution
- Discrete vs Discreet
- Binomial vs Normal Distribution
- Probability Distribution Function vs Probability Density Function
- Continuous vs Continual
- Dispersion vs Diffusion
- Finite vs Continuous Cell Lines
- Dispersion vs Skewness
- Central Tendency vs Dispersion
- Emission vs Continuous Spectrum
- Partition Coefficient vs Distribution Coefficient