## What evidence supports your hypothesis?

A hypothesis is usually a prediction based on some observation or evidence. Hypotheses must be testable, and **once tested, they can be supported by evidence**. If a statement is made that cannot be tested and disproved, then it is not a hypothesis.

## What is it called when a hypothesis is correct?

Answer has 9 votes. It can be correct to say ‘**I believe that my hypothesis is correct**‘. The use of the word ‘falsifiable’ is correct, but can cause some to think that the theory/hypothesis is a deliberate lie, or that the data used can be shown to be fraudulent.

## Why is probability important in hypothesis testing?

The P value is a number that tells us how unlikely our sample values are, given that the null hypothesis is true. A P value indicating that the sample results are not likely to have occurred, if the null hypothesis is true, **provides reason for doubting the truth of the null hypothesis**.

## How does the probability of a Type I error relate to the significance level in a hypothesis test?

**The probability of making a type I error is α, which is the level of significance you set for your hypothesis test**. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.

## Is the hypothesis correct if yes why if no why?

Answer: Explanation: Practically, hypotheses are answers to research questions which are formulated as yes/no or wh-questions. Therefore, the use of MAY or WILL would not be appropriate because the questions are in the form of does, do, is, are , etc. or in the form of how does, what is, and so on.

## Why are hypotheses never accepted as proven by scientists?

In science, a hypothesis is an educated guess that can be tested with observations and falsified if it really is false. You cannot prove conclusively that most hypotheses are true because **it’s generally impossible to examine all possible cases for exceptions that would disprove them**.

## When testing a hypothesis why don’t we set the probability of a Type I error to be extremely small explain?

A Type I error is when we reject a true null hypothesis. **Lower values of α make it harder to reject the null hypothesis**, so choosing lower values for α can reduce the probability of a Type I error. The consequence here is that if the null hypothesis is false, it may be more difficult to reject using a low value for α.

## Why does increasing sample size also increase the power of a test?

As the sample size gets larger, **the z value increases therefore we will more likely to reject the null hypothesis; less likely to fail to reject the null hypothesis**, thus the power of the test increases.

## What happens to the probability of making a Type II error as the level of significance decreases Why?

What happens to the probability of making a Type II error, β, as the level of significance, α, decreases? Why? **the probability increases**. Type I and Type II errors are inversely related.

## What increases the probability of a Type 2 error?

The probability of committing a type II error is equal to one minus the power of the test, also known as beta. The power of the test could be increased by **increasing the sample size**, which decreases the risk of committing a type II error.

## What happens to the probability of committing a Type I error if the level of significance is changed from a 0.01 to a 0.05 group of answer choices?

What happens to the probability of committing a Type I error if the level of significance is changed from α = 0.01 to α = 0.05? The probability of committing a Type I error **will increase**.

## How can you reduce the probability of a Type 2 error?

Once the level of significance is set, the probability of a type 2 error (failing to reject a false null hypothesis) can be minimized either by **picking a larger sample size or by choosing a “threshold” alternative value of the parameter in question that is further from the null value**.

## Why does increasing sample size decrease Type 2 error?

**If power increases then β must decrease**. So, if the power of a statistical test is increased, for example by increasing the sample size, the probability of committing a Type II error decreases. When we fail to reject the null hypothesis, can we accept the null hypothesis?

## Why does increasing sample size decrease type 2?

**As the sample size increases, the probability of a Type II error (given a false null hypothesis) decreases**, but the maximum probability of a Type I error (given a true null hypothesis) remains alpha by definition.