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Hypothesis Testing - Coggle Diagram
Hypothesis Testing
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- State a hypothesis about a population
- Before selecting a sample, a hypothesis to predict the characteristics the sample should have
- Obtain a random sample from the population
- Compare the obtained sample data with the prediction that was was made
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- Make a hypotheses about the value of μ (population mean. This is the Null Hypothesis
- Plug the hypothesized value in the formula along with the other values
- If a Z-Score near zero is produced, the hypothesis is correct. If an extreme value is produced, the hypothesis can be concluded as wrong.
The standard, Two-Tailed Hypothesis-testing procedure is the most unlikely used format, but the alternative used to specify either an increase or decrease in the population mean.
-One-Tail testa allow the research to reject the null hypothesis when the difference between the sample and the population is relatively small, provided the difference is in the specified direction
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The Z-Score Statistic that is used in the hypothesis test is the first specific example of what is called the Test Statistic. This indicates that the sample data is converted into a single, specific statistic that is used to test the hypothesis.
Type-II Error occurs when the sample mean is not in the critical region even through the treatment has an effect on the sample
-The consequences are not as serious as a Type-I Error, but this type of error means that the research data do not show the results that the researcher had helped to obtain -Type-II Error is represented by the Greek letter Beta (β)
Hypothesis Testing - a statistical procedure that allows researchers to use sample data to draw inferences about the population of interest
Cohen's d - A standard measure of effect size computed by dividing the sample mean difference by the sample standard deviation Cohen's d measures the distance between two means and is typically reported as a positive number even when the formula produces a negative value
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The second hypothesis is simply the opposite of the Null Hypothesis, Alhough it is also called the scientific or Alternative Hypothesis.
Type-I Error - - Occurs when a researcher rejects a null hypothesis that is actually true. This error means the researcher concludes that a treatment does have an effect when in fact It has no effec
When a researcher rejects the null hypothesis, there is a risk of a Type-I Error. Additionally, whenever a researcher fails to reject the null hypothesis, there is a risk of a Type-II Error