Statistics and Statistical Data is the study of the collection, organization, analysis, interpretation, and presentation of numerical data by the theory of probability, especially with methods for drawing inferences about characteristics of a population from examination of a random sample.
Nearly 2000 years ago, the study of statistics has begun. Now, it holds an essential position in the fields of economics, business, industry, and all branches of science. Students will come to use both statistics and data in scholarly research. Learn more about the statistical data, statistical data analysis from the further sections & enhance your subject knowledge.
What is Statistical Data in Math?
The study of statistical data starts with collecting data. On the basis of a method of collection, the data may be divided into two categories.
(i) Primary Data: If the researcher collects the data by personal observation and is only liable for their truth then the data is known as primary data.
(ii) Secondary Data: If the auditor prepares the data on the basis of information given by different sources like correspondence or publications then the data is called secondary data.
Types of Statistical Data Analysis
There are two widely utilized statistical methods under the techniques of statistical data analysis and they are as such:
1. Descriptive Statistics
This type of statistical data is a form of data analysis that is primarily used to explain, show, or summarize data from a sample in an expressive way. For instance, mean, median, standard deviation, and variance.
In short, it aims to describe the relationship between variables in a sample or population and provides a summary in the form of mean, median, and mode.
2. Inferential Statistics
In order to make conclusions from the data sample by using the null and alternative hypotheses, we use this method. And, also it is included with a probability distribution, correlation testing, and regression analysis.
In short, inferential statistics utilize a random sample of data, interpreted from a population, to make and describe inferences about the whole population
Difference Between Descriptive Statistics and Inferential Statistics
The given table make you understand the factual differences between descriptive statistics and inferential statistics;
S.No |
Descriptive Statistics |
Inferential Statistics |
1 |
Related with specifying the target population. |
Make inferences from the sample & also make them generalize as per the population. |
2 |
Arrange, analyze and reflect the data in a meaningful mode. |
Correlate, test, and anticipate future outcomes. |
3 |
The final results are described in the form of charts, tables, and graphs. |
Concluding results are the probability scores. |
4 |
Describes the earlier acknowledged data. |
It helps in making conclusions regarding the population which is over the data available. |
5 |
Measures of central tendency (mean, median, mode), Spread of data (Range, standard deviation, etc.) are the Deployed tool. |
Hypothesis testing, Analysis of variance, etc. are the Deployed tools. |
FAQs on Data and Statistics in Maths
1. What is sample or representative data?
A representative sample or representative data is a subset of a population that attempts to correctly match the characteristics of the larger group in the field of study. The relevant data is very large then we use samples. For instance, an auditorium of 20 students with 15 males and 5 females could generate a representative sample that might include 4 students: two males and two females.
2. What is Statistical Data Analysis?
Statistics include data acquisition, data interpretation, and data validation, and statistical data analysis is the way of managing various statistical operations, i.e. thorough quantitative research that endeavors to quantify data and implements some sort of statistical analysis.
3. What are the 4 Basics Steps for Statistical Data Analysis?
For analyzing any problem by using the statistical data analysis includes four basic steps;
1. Defining the problem
2. Accumulating the data
3. Analyzing the data
4. Reporting the outcomes