Contents
What are the two types of errors in hypothesis testing?
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.
What are the 4 outcomes of hypothesis testing?
time a statistical test is performed, one of four outcomes occurs, depending on whether the null hypothesis is true and whether the statistical procedure rejects the null hypothesis (Table 1): the procedure rejects a true null hypothesis (i.e. a false positive); the procedure fails to reject a true null hypothesis ( …
What is an example of a null hypothesis?
A null hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. In the example, Susie’s null hypothesis would be something like this: There is no statistically significant relationship between the type of water I feed the flowers and growth of the flowers.
What are the possible outcomes of testing a hypothesis?
There are two possible outcomes to a hypothesis test: Reject the null hypothesis, H0, in which case we have evidence in support of the alternative hypothesis. Not reject the null hypothesis, H0, in which case we do not have enough evidence to support the alternative hypothesis.
How do you write a null and alternative hypothesis example?
The actual test begins by considering two hypotheses. They are called the null hypothesis and the alternative hypothesis….Null and Alternative Hypotheses.
| H0 | Ha |
|---|---|
| equal (=) | not equal (≠) or greater than (>) or less than (<) |
| greater than or equal to (≥) | less than (<) |
| less than or equal to (≤) | more than (>) |
Which is the most important statistic in a confusion matrix?
Cells in a confusion matrix that a Frequentist is most concerned with. In the Frequentist approach to hypothesis testing, the most important statistic has traditionally been the p -value. The p -value is an estimate of the probability of observing an effect when the effect doesn’t exist.
What’s the difference between a hit and a confusion matrix?
Even though the proportion of Correct Rejections is the largest, the observation of an effect is simply too unreliable because there is a very high chance of it being a False Alarm instead of a Hit. Cells in a confusion matrix that a Frequentist is most concerned with.
How is recall calculated from a confusion matrix?
Most performance measures such as precision, recall are calculated from the confusion matrix. 1. What is a confusion matrix and why it is needed. 2. How to calculate a confusion matrix for a 2-class classification problem using a cat-dog example. 3. How to create a confusion matrix in Python & R. 4.
When do Bayesians use the confusion matrix?
In such situations, the chance of getting a False Alarm whenever an effect is observed becomes very high. This is where Bayesians try to account for what Frequentists miss out in their analyses, which I will be discussing next using confusion matrices.