Define the following terms with appropriate examples: a) Population b) Sample c) Inferential and Referential Statistics in python

 Define the following terms with appropriate examples:

a) Population b) Sample c) Inferential and Referential Statistics


Here are definitions and examples for the terms you mentioned:


a) Population:

In statistics, a population refers to the entire set of individuals, objects, or events of interest that we want to study and draw conclusions about. It represents the complete group we are interested in, from which we gather data. For example, if we want to study the average height of all adults in a country, the population would be all the adults in that country.


b) Sample:

A sample, on the other hand, is a subset of the population. It is a smaller representative group selected from the population with the intention of making inferences or generalizations about the larger population. The sample is used to estimate or draw conclusions about the characteristics of the population. Using the previous example, if we select and measure the height of a specific group of adults in the country, that group would be the sample.


c) Inferential and Referential Statistics:

Inferential Statistics:

Inferential statistics involves making inferences or generalizations about a population based on information or data collected from a sample. It is used to draw conclusions, make predictions, or test hypotheses about the population. Inferential statistics uses probability theory and statistical techniques to estimate population parameters, such as means, proportions, or correlations, based on sample data. For example, using inferential statistics, we can estimate the average income of all employed individuals in a city based on a sample of survey responses.


Referential Statistics:

Referential statistics, also known as descriptive statistics, is concerned with summarizing and describing the characteristics of a sample or a population. It involves organizing, presenting, and analyzing data to provide a clear and concise description of the dataset. Referential statistics includes measures such as central tendency (mean, median, mode) and measures of dispersion (variance, standard deviation). These statistics provide insights into the distribution, variability, and general features of the data. For example, calculating the mean and standard deviation of a sample's test scores provides descriptive information about the performance of that sample.


Python provides various libraries for statistical analysis and inference. Libraries like NumPy, pandas, and scipy offer functions and methods to perform statistical calculations, data manipulation, and hypothesis testing, facilitating both inferential and referential statistics in Python.






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