Distribution Of Data – Know About Types And Techniques

The distribution of data is a listing of all possible data values. It helps to measure the frequency of each variable. Mostly, in data distribution, the values are ordered from smallest to largest. In this way, charts and graphs become easy to read. In other words, based on the smallest to the largest values, the frequency of each variable in the charts or graphs becomes easy to measure. Often, we cannot gather all the necessary information about all variables involved in a study.

Hence, with different sampling techniques, we gather information that helps in drawing useful conclusion to the whole data set. To gather information from different sets of information, knowledge about the distribution is necessary.  Hence, this article will discuss the four most important probability distribution types and ways to identify them in statistics.

What Are The Four Types Of Distribution In Statistics?

In statistics, there are four main types of distributions namely Normal distribution, Poisson distribution, Binomial distribution, and Chi-square distribution. All these processes serve different purposes. Experts of a cheap dissertation writing service have discussed each in detail.  

Normal distribution

In this type, outcomes’ values are taken in the form of a continuous number. In simple words, the normal distribution of data is the type where the majority of the points in a dataset are relatively similar to each other. It means that it deals with data with fewer outliers at the low and high ends of the data range. It results in bell-shaped curves. For example, a statistical process dealing with a real number set will either be normal or continuous as it presents all the possible values within the real number. Likewise, a set of whole numbers and prime numbers are also the examples of normal distribution. 

In real life, continuous data is obtained through the setting of an experiment. For example, we are observing the temperature of a few days, measuring the heights of individuals belonging to certain age groups and calculating the solubility of a mixture of various substances, all results in continuous data which is the subject of normal data distribution. After taking all possible readings, we create a distribution table and then the probability density function that further helps in describing it. 

Chi-square distribution

The type of distribution of data that deals with the categorical data is called Chi-square distribution. The main purpose of Chi-square distribution is the testing hypotheses. It also has a limited number of applications in calculating intervals for population variance. Unlike Binomial and Normal distribution, Chi-square distribution is not as common in the modelling of a natural phenomenon. In simple, it is a statistical method to deal with continuous data with a certain degree of freedom. Researchers use this type of distribution of data with normal distribution. It describes the distribution of the sum of the square of random data variables.   

Binomial distribution

The binomial distribution summarises observations when experiments have the same probability of getting a specific value. The binomial distribution is different from the normal distribution based on a range of data points. In this type of distribution of data, there are no points between two discrete data points. Moreover, the discrete outcome is the one that has a limited number of possibilities. The most common graphical representation type for binomial distribution is histogram and bar graphs. At the same time, in a normal distribution, the data points have continuous ranges, hence, result in a bell-shaped graph. 

For example, if someone rolled a disc, then the data will be discrete (limited number of outcomes), and the distribution of data will be binomial distribution.  

Poisson distribution

Poisson is also a data distribution process that deals with discrete data (having a few possible outcomes). Both Binominal and Poisson distribution deal with discrete points, but they have some differences as well. For Poisson distribution, the probability of a given number of events is measured in terms of a specific timeframe. Moreover, in Poisson distribution, the given numbers of events are also measured with a particular distance, volume and area other than time. A common real-life example for this type of distribution of data includes the calculation of a specific number of customers in a shirt and tie shop between 11:00 am to 8:00 pm.  

How do you find the distribution of data in statistics?

Mostly, students are more familiar with the normal distribution of data. It is the most common and well-organised method. But students feel confused when they have to deal with non-normal data. Hence, they must require ways to find results in statistics.

Probability plots are probably the best possible way to find the distribution of data in statistics. It helps recognise whether your research follows a particular pattern of distribution as well as which type of distribution it really falls in. In such plots, if your research follows a straight line, which means the distribution fits your research. This way is simple and easy to visualise.     

Once we plot the raw data in a graph, distribution tests allow us to find the distribution of data. Like all statistical hypothesis tests, distribution hypothesis tests also have a null and alternative hypothesis. The purpose of this test is to identify whether a sample was drawn from a population that follows a hypothesised probability distribution. For example, the null hypothesis is ‘the sample follows the hypothesised distribution’ and the alternative hypothesis suggests ‘the sample does not follow the hypothesised distribution’. The smaller p-value for a hypothesis leads to the rejection of the hypothesis. 

Final Thoughts:

Consequently, the distribution of data is a statistical method of data interpretation. Its main goal is to find the occurrence of a particular event in an experiment. Depending upon the data, the distribution is of four types involving normal, binomial, chi-square, and poisson distribution. They all help find the it in continuous and discrete forms of data. But to find the it in statistics, the Plotting and Distribution tests are of prime importance. The graphical representation helps in the visual estimation of the proposed type of it. On the other hand, the distribution tests, like other statistical tests, measure the P-value for each hypothesis (null and alternative). Depending on the results of hypothesis testing, you can find it in statistics.