We start to learn statistics from school. In school, we learn statistics as a part of maths. But later, at an advanced level, statistics is a very important subject. Like other subjects, statistics also has its own importance. Most of the students make their career in this field. Statistics is important in almost every field. Like weather forecasting, medical, finance, etc. That is why students choose statistics as their major subject.

Moreover, statistics subjects involve different important topics. One of the main topics is statistical distribution. Here, we will discuss this topic. We will also learn types of statistical distribution. So, let’s start with the understanding of statistical distribution.

**What is Statistical Distribution?**

A statistical distribution is a collection of values for a variable. It shows the observed or theorized frequency of occurrence.

**Types Of Statistical Distribution**

**Normal Distribution**

The bell curve is another term for the normal distribution. It happens naturally in a variety of situations.

Several groupings follow the normal distribution pattern.

- Errors in measurement.
- Points on the test.
- Salaries.
- People’s height.
- Blood pressure
- IQ tests.

**Properties **

- At the center, the curve stays symmetric.
- 1 is the area under the curve.
- The mean, median, and mode are all set to the equal value.
- Half of the value is on the left, while the other half is on the right.

**Uniform Distribution**

The uniform distribution is the most fundamental type of continuous distribution. It has a constant chance of forming a rectangular distribution. It also means that each and every value has the equal distribution length. Which has an equal chance of happening. This function, on the other hand, belongs to the category of maximum entropy probability distributions.

**Characteristics**

- The density function adds up to unity.
- Its input functions are all equally weighted.

**Bernoulli Distribution**

Bernoulli distribution is a discrete probability distribution. It represents a random trial with two outcomes. A single coin toss, for example, is a special case with the number n = 1.

**Characteristics**

- The number of trials that are performed in a single experiment must be determined.
- Each trial must result in one of two outcomes: success or failure.
- Each experiment’s success probability should be the same.
- The trials should be independent of one another. It means that the outcome of one trial is unaffected by the outcome of the other.

**Binomial Distribution**

Under a set of assumptions or parameters, a probability distribution concludes the value that has one of two independent values. Furthermore, the assumptions of the binomial distribution must provide a single result with the same chance of success. And each path must be distinct from the others

**Properties**

- Each of the independent trails. An experiment has two outcomes: success or failure.
- The bi-parametric distribution is another name for the binomial distribution. As two parameters, n and p, are used to classify it.

**Poisson Distribution**

When you know the value of an event, you may use it to forecast a particular probability. The Poisson distribution tells us how likely a certain number of events will occur in a given amount of time.

**Properties**

- The random variable’s expected value and variance are equal to λ.
- The Poisson distribution’s expected value is divided by underlying the product of intensity and exposure.
- “m” represents the Poisson distribution’s mean.

**Beta Distribution**

It refers to a group of continuous probability distributions that fall inside the [0,1] interval. They are denoted by the letters alpha and beta. This model is also used for the model that has an uncertainty in the chance of a random experiment succeeding. It also has a strong tool that uses basic statistics to calculate the amount of confidence in the completion time.

**Properties**

These distributions can be satisfied by several properties:

The following are the phrases used to describe the central tendency:

- Mean
- Mean Mean
- Mode
- Median
- Geometric Mean

**Log-Normal Distribution**

Rather, we may state that ln(x) has a normal distribution and that the variable x has a log-normal distribution.

**Properties**

- The anticipated value or the mean of distribution provides information about what an average person would expect given a series of trial numbers.
- Another aspect of central tendency is the median of a log-normal distribution. It is important for outliers. It enables the means to lead.
- The mode of a distribution is the value that has the greatest chance of occurring.
- Because the square root of the variance and the standard deviation has the same data unit. They are both helpful.

**Conclusion**

The above blog has provided you with detailed different types of statistical distribution. These types also help students in understanding the difficult terms. So, I hope you read the blog carefully. And understands the terms well. It will help you when you get statistics assignments.