Inferential statistics

PARAMETERS

A parameter is a measure that describes a population it is usually denoted by Greek letters parameter is a number that describes something about the whole population parameter in statistics Statistics is important is an important component of any statistical analysis in simple words parameter is any numerical quantity that characterizes a given population or something or some aspects of it.

Statistics

The statistic is a measure that describes a sample it is usually denoted by Roman letters

statistics is a number that describes some characteristics of a sample the value of statistics can be computed directly from the sample data we use statistics to estimate an unknown parameter

Figure 1

Figure that shows a statistical model

Parameter

If value is usually a numerical value that describes a population Derived from the measurement of the individual in the population statistics a value usually a numerical value that describes a sample derived from the measurement of individuals in the sample

Why are parameters important in statistics?

Parameters in statistics in an important component of any statistical analysis
In simple words, parameter is any numerical quantity that characterizes a given population or some aspect of it.

Inferential statistic

Enables you to make an educated guess about a population parameter based on a statistic computed from a sample randomly drawn from that population
Inferential statistics deals with the process of inter information about a population
Inferential statistics are often used to compare the difference between the treatment groups
Such inferential information is subject to a measure of uncertainty
Because the sample size is typically significantly smaller than the size of the population
There are many types of inferential statistics and each is appropriate for a specific research design and sample characteristics

Figure 2

The figure shows inferential statistics

Descriptive statistics	Inferential statistics
Describe or summarize the data of a target population	Use data to make inferences or generalizations about the population
Describe the already known	Make a conclusion for a population that is beyond available data
Organize, analyze and present data in a meaningful manner	Compare, test and predict future outcomes
The final result is shown in form of a table and graphs	The final results are the probability scores
Tools measures of central tendency and dispersion	Tools: hypothesis tests