Critical values are the threshold values that determine the boundaries of acceptance or rejection regions in hypothesis testing, based on a chosen significance level. They are essential in deciding whether to reject the null hypothesis, providing a benchmark for statistical significance in inferential statistics.

Critical Values

Critical values

threshold values

Acceptance regions

rejection regions

Hypothesis testing is a statistical method used to make decisions about the properties of a population based on a sample. It involves formulating a null hypothesis and an alternative hypothesis, then using sample data to determine which hypothesis is more likely to be true.

hypothesis testing

The significance level, often denoted by alpha, is the threshold for determining whether a statistical hypothesis test result is statistically significant. It represents the probability of rejecting the null hypothesis when it is actually true, and is commonly set at 0.05 or 5%, indicating a 5% risk of concluding that a difference exists when there is no actual difference.

significance level

The null hypothesis is a fundamental concept in statistical testing that posits no effect or relationship between variables, serving as a default or baseline assumption to be tested against. It is typically rejected or not rejected based on the strength of evidence provided by sample data, guiding researchers in making inferences about the population.

null hypothesis

Statistical significance is a measure that helps determine if the results of an experiment or study are likely to be genuine and not due to random chance. It is typically assessed using a p-value, with a common threshold of 0.05, indicating that there is less than a 5% probability that the observed results occurred by chance.

statistical significance

Inferential statistics involves using data from a sample to make inferences or predictions about a larger population, allowing researchers to draw conclusions beyond the immediate data. It relies on probability theory to estimate population parameters, test hypotheses, and determine relationships between variables, providing a framework for making data-driven decisions in the presence of uncertainty.

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