WHAT IS STATISTICS

Many people shy away or take to their heel when the term 'statistics' is mentioned. However, that shouldn't be the case. Statistics is just a regular field like any other that deals with statistical information. In case you have a query regarding statistics help, feel free to contact us today. Later in this article, we will have a look at what is statistics.

Definition of Statistics

This is a field of science that is primarily concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data. One surprising fact about the precedent is its high interdisciplinary factor in finding applicability in virtually all scientific fields and research questions.

It motivates the development of new statistical methods and the theories which elevate the study of math and computational tools. It brings out the other meaning of statistics. It alludes that statistics involves the computation of quantity with the help of statistical methods. Well, this can imply a statistical parameter income of a group and the median age of a population.

Statistics falls under two fundamental notions, uncertainty and decision making related to the same, and variability. These fundamentals seem different; however, they link to a common body of heritage and knowledge.

Uncertainty is well understood by the probability concept, which is the measure of the likelihood that an event will occur in a random experiment or trial. On the other side, variability is well explained using variables. A variable is anything that can change or take a different form. In statistics, variables fall into two types.

The first type is the dependent variable, which is anything that is measured. An independent variable is defined by anything that can change or differ between situations that can influence the dependent variables.

Types of Statistics

Statistics being an interdisciplinary discipline is broad in content. The statistical information gathered is vital in solving this day and age's problems. The subject matter falls under two types as follows.

Descriptive Statistics

This type is used to consolidate data into single representative values. These representative values are useful in describing the entire set of data. They also aid in summarizing the individual values in a data set.

Inferential Statistics

It can be crowned as a standard multi-task type of statistics. Unlike descriptive statistics, it can perform two different tasks. The first significance is they use statistical tests to analyze the relationships between variables. The second task is it allows one to make inferences and assumptions about the data collected relative to some larger group under study.

In-Depth knowledge of the group you are studying is vital. One needs to know the size of the group. With the help of a smaller group as a sample, the statistician is likely to jot down some clue of what is expected to happen in the large group.

Quantity and Quantitative variables

Qualitative Variables

Well as elaborated above, a variable is anything that can change or alter its form to a different physical state. qualitative variables involve the levels that base on distinct categories. They lack numerical figures and meanings attached to their standards. Thus they cannot express the differences in ratios or amounts. However, they represent the difference in forms such as the color of the hair, race, class, political orientation and so forth.

Quantitative variables

It includes the use of numbers in the levels. This type of variables is where levels are numerical and can be used to express differences in ratios and amounts. Some basic examples of quantitative variables include temperature, age, height, length and many more.

Hypothesis

One advantage of statistics as a subject is its use in evaluating research papers. It is essential in designing of data, collection, and application of statistical procedures in determining whether it supports the question. This question is known as the hypothesis.

Statistics helps researchers during research by supporting the theory of the work. The hypothesis is a clue of what one is likely to expect upon completion of the study. Hypothesis testing is divided into two.

• An alternate hypothesis asserts that there is a possible relationship between the two variables supporting the notion.
• The null hypothesis explains the difference between two variables citing no statistical significance. It tries to discredit or disapprove the result.
• Importance of Statistics

The high interdisciplinary field is the science of learning from the numerical info, well known as data. It plays an exemplary role in shaping the universe. With the help of other subjects, it provides remedies and solutions to the many challenges faced in our daily lives. Proper knowledge of the subject helps one to use the appropriate methods to collect the data, employ the correct analysis and effectively present the results.

It is an array of knowledge that allows one to articulate data reliably. With this, you can evaluate claims based on quantitative evidence and helps in differentiating between reasonable and dubious conclusions. This is critical in providing accurate analyses and predictions.

Statistics is also essential in other fields. The vast amount of data available delivers solutions to problems. Chances are high when in need of research as it provides new findings which require the knowledge of statistical methodology and understand how it works influences other disciplines.

Knowledge in the subject matter aids statisticians to avoid common pitfalls. The experience not only provides a guideline to follow, but it also enables them to detect a possible fake out of data that might pose to be confusing.

This process involves building a study design, choosing and measuring the suitable variables devising the sampling technique and sample size and numerous issues. The overall quality depends on the entire chain of events. Any sign of a single weak link is likely to deteriorate the research process as a whole.

Probability and statistics work well in the banking sector. It is logic to say that the likeliness of the number of customers depositing and the number of the same customers withdrawing those funds is not likely to be equal. Therefore, bankers use this approach to estimate the number of deposits made and their claims for a specific day.

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