Descriptive statistics are statistics that quantitatively define or précis structures of a collection of information Descriptive statistics are renowned from inferential statistics or inductive statistics, in that descriptive statistics goal to précis a sample, before use the data to learn about the population that the sample of data is understood to represent. This usually means that descriptive statistics, unlike inferential statistics, are not established on the basis of probability theory. When a data exploration draws its main decisions using inferential statistics, descriptive statistics are usually also existing. For example, in papers writing on human subjects, usually a table is comprised giving the overall sample size, sample sizes in important subcategories, and demographic or clinical features such as the average age, the amount of subjects of each sex, the proportion of subjects with linked to comorbidities etc.

Some actions that are usually used to define a data set are measures of central tendency and measures of variability. Measures of central tendency contain the mean, median and mode, whereas measures of variability comprise the standard deviation or variance, the maximum and minimum values of the variables, kurtosis, and skewers.

Branches of Statistics:-

All students of statistics should know about the different branches of statistics to properly recognize statistics from a more general opinion. Frequently, the kind of job or work one is involved in hides the other features of statistics, but it is very important to know the whole idea behind statistical analysis to completely appreciate its importance and attractiveness.

Descriptive Statistics:-

Descriptive statistics are short-term descriptive coefficients that summarize assumed data set, which can be also a depiction of the whole population or a sample of it. Descriptive statistics are not working into measures of central tendency and measures of variability or spread.

Descriptive statistics are used to define the basic structures of the data in a study. They deliver simple précises about the sample and the measures.

Inferential statistics makes ideas about populations using data drawn from the population. In its place of using the entire population to collect the data, the statistician will gather a sample or samples from the lots of inhabitants and make suggestions about the whole population using the sample.

Inferential Statistics:-

Inferential statistics, as the name recommends, includes drawing the correct conclusions from the statistical analysis that has been done using descriptive statistics. In the end, it is the implications that make studies important and this feature is distributed with in inferential statistics.

Most predictions of the upcoming and generalizations about a population by learning a smaller sample originate under the purview of inferential statistics. Most social sciences trials deal with teach a small sample population that helps define how the population in general acts. By designing the right trial, the investigator is able to draw conclusions related to his study.

Though drawing inferences, one desires to be very careful so as not to draw the wrong or prejudiced conclusions. However this seems like a science, there are ways in which one can manipulate studies and results through various means. For example, data dredging is gradually becoming a problem as computers grip loads of information.