# Statistical Data definition, types and requirements

## Definition of statistical data

Statistical data are the outcomes or the observations which occur in scientific experiments or an investigation. To conduct any analysis it is must to have some data. Without data we can not think about research or statistical analysis. In statistics, data plays a vital role in all the fields and all the theories and measurement. Measure of central tendency (mean, median, mode), measure of dispersion (variance, mean deviation, standard deviation, etc) are some statistical measure by which we find the different characteristics of the data.

For example, In a garments factory, we want to find the female workers’ height and weight. If we consider the size in feet and weight in kilograms, then we get some numerical values, which are the numerical data.

### Types of statistical data

All statistical data may be classified into two categories.

• Qualitative: Gender, Education status, Marital status, etc.
• Quantitive: Age, height, weight, etc.

we can divide data into two categories depending on the data collection as,

• Primary data: Primary data are those which are collected from the units or individuals directly, and these data have never been used for any purpose earlier.
• Secondary data: The data, which had been collated by some individual or agency and statistically treated to draw certain conclu­sions. The same data are used and analyzed to extract some other information, which is termed as secondary data.

### Methods of data collection

Following are the methods of collection of data:

• Direct personal inquiry method;
• Indirect oral investigation;
• By filling of schedules;
• By mailed questionnaires;
• Information from local agents.
• By old records; rind
• By the direct observational method,

### Requirements of reliable statistical data

• It should be complete.
• It should be consistent.
• It should be accurate, and
• It should be homogeneous with respect to the unit of information.

Statistical data are defined under some random variable where one random variable contains same characteristics of data. Such as height is random variable where we include all the data which represents height. We collect data from a certain area which is called study area. All the data present in the study are is called population. Generally population size is very large that’s why we collect the representative part from the population which is called sample.