Inferential statistics is a branch of statistics that deals with the analysis and interpretation of data in order to make inferences or draw conclusions about a larger population based on a sample of data. It involves using statistical techniques to make predictions, test hypotheses, and estimate population parameters.
Inferential statistics is often used in scientific research, medical studies, market research, and other fields where it is not feasible or practical to collect data from an entire population. Instead, a sample of data is collected, and inferential statistics are used to draw conclusions about the population based on that sample.
Steps:
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Formulate a hypothesis: The researcher formulates a hypothesis that can be tested using statistical techniques.
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Select a sample: The researcher selects a representative sample of the population to study. The sample must be large enough and properly randomized to ensure that it is representative of the population.
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Collect data: Once the sample has been selected, the researcher collects data using appropriate methods.
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Analyze the data: The researcher analyzes the data using appropriate statistical techniques to test the hypothesis.
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Draw conclusions: Based on the results of the analysis, the researcher can draw conclusions about the population from which the sample was drawn.
Inferential statistics can be used to test hypotheses, estimate population parameters, and make predictions about future events. It is important to note that inferential statistics can be subject to errors and biases, and it is important to use appropriate statistical techniques and to properly interpret the results.