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Statistical Errors
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Type I Error (False Positive)
Occurs when a true null hypothesis is incorrectly rejected.
Type II Error (False Negative)
Happens when a false null hypothesis fails to be rejected.
Power of a Test
The probability that the test correctly rejects a false null hypothesis.
Significance Level ()
The probability of making a Type I error; the threshold for deciding when to reject the null hypothesis.
p-value
The probability, under the null hypothesis, of obtaining a result at least as extreme as the one actually observed.
Confidence Level
The percentage of confidence intervals (constructed under the same conditions) that contain the true parameter value.
Effect Size
A quantitative measure of the magnitude of a phenomenon, used to determine the sample size required for an experiment.
Sampling Error
The difference between a sample statistic and its corresponding population parameter, caused by the fact that the sample is not a perfect representation of the population.
Standard Error
The standard deviation of the sampling distribution of a statistic, most commonly the mean.
Bias (in Estimation)
A systematic deviation of an estimator's expected value from the true value of the parameter being estimated.
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