Void symbol in the test 2 effortlessly

Aug 6th, 2022
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How to Void symbol in the test 2

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how do you know when youre supposed to use a one-tailed test or a two-tail test lets say when youre solving problems associated with hypothesis testing how do you know well in this video were going to talk about that so lets say that a company manufactures potato chips and that the average mass of each potato chip bag is a hundred grams so that is going to be the null hypothesis where the mean mu is a hundred grams now lets say that an employee believes this answer to be different he believes that the mean is not 100 grams so this is going to be the alternative hypothesis now whenever you have the situation whenever the alternative hypothesis doesnt equal some value youre going to have a two-tailed test so lets draw a picture of that so we have a normal distribution and were going to shade the area on the left and on the right so this is a two-tailed test the shaded area represents the rejection region the area that is not shaded is the fail to reject region now the z values

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A Type I error is often represented by the Greek letter alpha () and a Type II error by the Greek letter beta ( ). In choosing a level of probability for a test, you are actually deciding how much you want to risk committing a Type I errorrejecting the null hypothesis when it is, in fact, true.
Null Hypothesis Symbol In statistics, the null hypothesis is usually denoted by letter H with subscript 0 (zero), such that H0. It is pronounced as H-null or H-zero or H-nought.
A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.
A type II error occurs when one rejects the alternative hypothesis (fails to reject the null hypothesis) when the alternative hypothesis is true. The probability of a type II error is denoted by *beta*.
In a two-tailed test of the population mean, the null hypothesis claims that the true population mean is equal to a given hypothetical value 0. A type II error occurs if the hypothesis test based on a random sample fails to reject the null hypothesis even when the true population mean is in fact different from 0.
A Type 2 error is when a false null hypothesis is accepted.
Type II errors In statistical terms, this occurs when the null hypothesis is incorrectly accepted and this causes a false-negative result. Type II errors are more likely to occur when sample sizes are too small, the true difference or effect is small and variability is large.
Therefore, the probability of committing a Type II error is not uniquely pinned down, but it depends on the true variance. The function that links the value of the true variance to the probability of a Type II error is called power function.
H1: 0 , where a decrease is hypothesized and this is called a lower-tailed test; or. H1: 0, where a difference is hypothesized and this is called a two-tailed test.

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