Contents

- 1 What are the two decisions that you can make from performing a hypothesis test what are the two decisions that you can make from performing a hypothesis test select all that apply?
- 2 What are the two decisions that you can make from performing a hypothesis test chegg?
- 3 What are the two divisions that you can make from performing a hypothesis test?
- 4 What are the two decisions that you can make from performing a hypothesis test a reject the alternative hypothesis accept the null hypothesis b accept the null hypothesis accept the alternative hypothesis c reject the null hypothesis fail to reject the null hypothesis d make a type?
- 5 How do you determine if a hypothesis is two-tailed?
- 6 What is a formal hypothesis test?
- 7 Does failing to reject the null hypothesis mean the null hypothesis is true?
- 8 What is the P-value approach when testing a hypothesis?
- 9 How should you interpret a decision that rejects the null hypothesis?
- 10 What is the typical level of significance for a hypothesis test in behavioral research?
- 11 How do you know if something is sufficient evidence?
- 12 What is the probability of committing a Type I error?
- 13 What does reject the null hypothesis mean?
- 14 Which type of error in hypothesis is more dangerous?
- 15 What is the difference between a Type I error and a Type II error?

## What are the two decisions that you can make from performing a hypothesis test what are the two decisions that you can make from performing a hypothesis test select all that apply?

6.1 – Type I and Type II Errors. When conducting a hypothesis test there are two possible decisions: reject the null hypothesis or fail to reject the null hypothesis. You should remember though, hypothesis testing uses data from a sample to make an inference about a population.

## What are the two decisions that you can make from performing a hypothesis test chegg?

reject the alternative hypothesis reject the null hypothesis fail to reject the null hypothesis make a type 1 error accept the null hypothesis accept the alternative hypothesis make a type 2 error fail to reject the alternative hypothesis.

## What are the two divisions that you can make from performing a hypothesis test?

So, when you perform a hypothesis test, you make one of two decisions: 1. reject the null hypothesis or 2. fail to reject the null hypothesis.

## What are the two decisions that you can make from performing a hypothesis test a reject the alternative hypothesis accept the null hypothesis b accept the null hypothesis accept the alternative hypothesis c reject the null hypothesis fail to reject the null hypothesis d make a type?

There are two options for a decision. They are “reject H _{}” if the sample information favors the alternative hypothesis or “do not reject H _{}” or “decline to reject H _{}” if the sample information is insufficient to reject the null hypothesis.

## How do you determine if a hypothesis is two-tailed?

A two-tailed test will test both if the mean is significantly greater than x and if the mean significantly less than x. The mean is considered significantly different from x if the test statistic is in the top 2.5% or bottom 2.5% of its probability distribution, resulting in a p-value less than 0.05.

## What is a formal hypothesis test?

A falsifiable hypothesis is a statement, or hypothesis, that can be contradicted with evidence. In empirical (data-driven) research, this evidence will always be obtained through the data. In statistical hypothesis testing, the hypothesis that we formally test is called the null hypothesis.

## Does failing to reject the null hypothesis mean the null hypothesis is true?

In a similar way, a failure to reject the null hypothesis in a significance test does not mean that the null hypothesis is true. It only means that the scientist was unable to provide enough evidence for the alternative hypothesis. As a result, the scientists would have reason to reject the null hypothesis.

## What is the P-value approach when testing a hypothesis?

The P-value approach involves determining “likely” or “unlikely” by determining the probability — assuming the null hypothesis were true — of observing a more extreme test statistic in the direction of the alternative hypothesis than the one observed.

## How should you interpret a decision that rejects the null hypothesis?

Interpret the decision in the context of the original claim. If the claim is the null hypothesis and H₀ is rejected, then there is enough evidence to reject the claim. If H₀ is not rejected, then there is not enough evidence to reject the claim.

## What is the typical level of significance for a hypothesis test in behavioral research?

The likelihood or level of significance is typically set at 5% in behavioral research studies. When the probability of obtaining a sample mean would be less than 5% if the null hypothesis were true, then we conclude that the sample we selected is too unlikely, and thus we reject the null hypothesis.

## How do you know if something is sufficient evidence?

If the p-value is less than α, we reject the null hypothesis. If the probability is too small (less than the level of significance), then we believe we have enough statistical evidence to reject the null hypothesis and support the alternative claim.

## What is the probability of committing a Type I error?

The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis. A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis.

## What does reject the null hypothesis mean?

If there is less than a 5% chance of a result as extreme as the sample result if the null hypothesis were true, then the null hypothesis is rejected. This does not necessarily mean that the researcher accepts the null hypothesis as true—only that there is not currently enough evidence to conclude that it is true.

## Which type of error in hypothesis is more dangerous?

Type I errors in statistics occur when statisticians incorrectly reject the null hypothesis, or statement of no effect, when the null hypothesis is true while Type II errors occur when statisticians fail to reject the null hypothesis and the alternative hypothesis, or the statement for which the test is being conducted

## What is the difference between a Type I error and a Type II error?

In statistics, a Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s actually false.