inferential statistics
Methods used to make inferences from a small group of observations, called a sample. These inferences are then applied to a larger group of individuals, known as a population. Typically, the researcher wants to make statements about the larger group but cannot make all of the necessary observations.
Inferential statistics is a branch of statistics that helps researchers draw conclusions about a population based on a sample of data. It involves using probability and statistical techniques to analyze collected data and make inferences about larger populations in order to make generalizations or predictions.
There are two main types of inferential statistics: estimation and hypothesis testing. Estimation involves using sample data to estimate population parameters, such as mean, variance, and standard deviation. Hypothesis testing involves using sample data to test an assumption or hypothesis about a population, typically by comparing sample statistics to population parameters.
Inferential statistics is used in a broad range of research fields, including social sciences, healthcare, business, and engineering. It is important to use inferential statistics to ensure that research findings are generalizable and reliable. However, it is important to note that inferential statistics do not prove causation, but rather provide evidence for or against a hypothesis. It is also important to carefully consider factors such as sample size, probability sampling, statistical significance, and effect size when conducting inferential statistics.
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