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A standard statistical procedure involves the collection of data leading to test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a “false positive”) and Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a “false negative”). Multiple problems have come to be associated with this framework, ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis.

In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics such as “all people living in a country” or “every atom composing a crystal”. Ideally, statisticians compile data about the entire population (an operation called census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data (like income), while frequency and percentage are more useful in terms of describing categorical data (like education).

Standard statistical analysis is a complex and detailed process involving the collection of data, incorporation of this data into a correlational analysis, and the subsequent comparison of the analysis to an idealized null hypothesis of no relationship between two or more data sets. The data collected is processed using various tests, with the ultimate goal being to prove — or disprove — correlation between dependent and independent variables.

A standard statistical procedure involves the collection of data. This process includes randomly sampling the population of interest, with each observation having a known chance of being selected. A statistician attempts to make inferences about the collected data by accounting for randomness in the sample, achieving an estimator that has desirable statistical properties, such as efficiency or unbiasedness under ideal conditions.

Statistics is a set of well-defined mathematical techniques used for analyzing and interpreting data. The applied methodologies of the subject include the discipline, abstraction and formalization of much information, e.g. in terms of probability theory and mathematical statistics. Statistics is the science that allows us to find meaningful patterns in large collections of data; we can then draw inferences from these patterns and predict the future. These applications predict everything from natural phenomena (weather, climate change) to financial markets (consumer spending, interest rates) and sports (winning teams, MVP players…).

There are several statistical procedures which can be used in the test of statistical hypotheses. It depends on three major factors: (1) The type of the underlying phenomena; (2) The state-of-knowledge about such phenomenon; and (3) The particular nature of the problem at hand. A statistical hypothesis is then either rejected or not rejected, based on comparison with an idealized null hypothesis. An inference is then made – it could be a decision to reject the hypothesis and accept the null hypothesis, accept both for now and continue further research, or reject both and conclude that there is insufficient evidence to support either hypothesis.

Statistics is a method of collecting, analyzing, interpreting and presenting data, in order to apply data to solve different purposes. Statistics is the science of learning from data. It is often referred to as the backbone of all sciences in which raw information can be translated into meaningful knowledge in any sphere. Statistics involves the compilation, analysis and interpretation of data in large number, which is then presented in practical forms that helps the user to take the best decisions  for profit maximization or penetration into new markets.  It helps making your business more profitable. Big businesses use statistical models to help them understand their customers better and how to better serve their customer needs.

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