Branches of Statistics You Should Know. Statistics is a crucial concept we use in our daily life. In various fields such as banking, astronomy, weather forecasting, business, and many more, statistics are used widely to make effective decisions by comparing data.
If you are a statistics student, you should know about the statistics applications, importance, and branches of statistics.
Statistics is all about collecting the data from various sources, analyzing this data, and comparing this data with the past data to measure the final value or result.
Based on these results, owners of various organizations make decisions. These results are driven under the different branches of statistics.
This blog will discuss branches of statistics that deal with different problems and solve them with other concepts.
Definition of Statistics
Statistics is a branch of mathematics that deals with different data types to extract valuable information for effective decision-making. It concerns the data collecting, analyzing, interpreting to aid the various entities in deciding what to do next.
It uses basic mathematical concepts and equations to solve problems for different purposes.
Branches Of Statistics
There are two main branches of statistics: Descriptive Statistics and Inferential statistics, which work on different principles. However, both are equally important and used in the scientific analysis of data.
Let’s talk about these branches-
Descriptive Statistics
Descriptive statistics work on the illustration and collection of data that is the first part of scientific analysis. This type of statistics is not as easy as it seems; it demands the proper concentration while designing the experiments and focusing on the group.
The statisticians need to avoid the biases that are not important for the experiment or need deep analysis.
We use different descriptive statistics in the other areas of study, such as measuring central tendency (mean, mode, median) and variabilities.
Descriptive statistics focus on the small data groups and represent the data by graphs and charts.
Tools used in Descriptive Statistics
Dispersion: It is used to calculate the dispersion range (extension of data from the center point) or standard deviation. It indicates low dispersion( tight cluster, values surrounding the center) and a high dispersion( loose collection, data values are far from the center).
Central Tendency: It helps determine the center of the data group with the help of mean or median.
Skewness: When we need to determine the order of the values if it is symmetric or skew-symmetric, we use this tool. The order is represented in the form of graphs or numbers.
Inferential Statistics
Unlike Descriptive statistics, inferential statistics focus on a large population. Inferences are made from the sample we draw from a large number of people and their reviews. In inferential statistics, we conclude the sampled data and generalize it.
In this case, the sample affects the inferential process, so the models should be accurate and reflect the population.
Here we need to express the number of people we will study, find out and describe samples, and adequately analyze these data to avoid errors in sampling.
It includes
Estimating Parameter: Use data samples for the conclusion. For instance, we have been feeling sick for the past two days, and the doctor prescribed us a blood test and took blood samples from our body to identify the disease; in the same way, samples are taken out from the different types of data to estimate the output.
Hypothesis Tests: Focus on research to decide the population with the help of the question’s answers in a survey. It helps to make predictions and estimations.
For example, outside a cinema, if we ask the people how the movie was, we can estimate the movie’s performance, good or bad, with their answers. In the same manner, hypothesis works.
What is the similarity between descriptive and inferential statistics?
Both descriptive and inferential statistics work on different principles and concepts. But when we talk about the similarity between these two, both depend on the corresponding datasets.
Otherwise, descriptive statistics depend on the individual’s points of datasets, and inferential statistics rely on the group of data to generalize the population.
Standard terms used in Statistics
In statistics, we use terms that confuse us. We understand it in some other context, such as sample, population, etc. Sometimes readers get confused about what the writer is talking about. Below are the terms that people do not understand easily.
Population: Most of you will be thinking that the population means we are talking about the number of people.
But no, it does not imply only the number of people but also refers to students of a school, rainfall determination, batch batteries, phone calls, number of products, etc.
A population is a group of data from where we collect the information. So, a source of data can be called population.
Sample: When we select a part of the population randomly, it is known as a sample. The model should define all the characteristics of the people to conclude. Meaningless data groups or part of the population can not be considered as samples.
Conclusion
Statistics is an essential branch of mathematics that deals with the scientific analysis for various predictions and estimations. Statistics contribute to many fields where we need forecast or future trends and the present value of any case.
This blog has discussed the definition and branches of statistics with the standard terms and tools used in the different statistics. I hope this article will be helpful in terms of searching definitions, concepts, and similarities in the branches of statistics.
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