Understanding Null and Alternative Hypotheses in Statistical Testing

1. What does 'H0' represent in statistical hypothesis testing?

Null Hypothesis

Confidence Interval

Type I Error

Alternative Hypothesis

In statistical hypothesis testing, H0 represents the null hypothesis, which is the assumption that there is no significant difference or relationship between variables. The alternative hypothesis is the opposite of the null hypothesis, suggesting that there is a significant difference or relationship. Type I Error refers to incorrectly rejecting the null hypothesis when it is actually true. Confidence Interval is a range of values that is likely to contain the true value of a population parameter.

Explanation

In statistical hypothesis testing, H0 represents the null hypothesis, which is the assumption that there is no significant difference or relationship between variables. The alternative hypothesis is the opposite of the null hypothesis, suggesting that there is a significant difference or relationship. Type I Error refers to incorrectly rejecting the null hypothesis when it is actually true. Confidence Interval is a range of values that is likely to contain the true value of a population parameter.

2. More than 0.05 or 5%?

The correct answer suggests that there is no evidence of a real difference or association in the population. This conclusion is drawn when the p-value is greater than 0.05 or 5%. The incorrect answers provide alternative interpretations of the statistical significance test outcomes.

Explanation

The correct answer suggests that there is no evidence of a real difference or association in the population. This conclusion is drawn when the p-value is greater than 0.05 or 5%. The incorrect answers provide alternative interpretations of the statistical significance test outcomes.

3. What is the term used to describe the hypothesis stating that there is a difference/association in the population?

Statistical Hypothesis

Null Hypothesis

H0

H1

The Alternative Hypothesis is the term used to describe the hypothesis suggesting that there is a difference or an association present in the population being studied.

Explanation

The Alternative Hypothesis is the term used to describe the hypothesis suggesting that there is a difference or an association present in the population being studied.

4. I FAIL TO REJECT THE NULL HYPOTHESIS..

The sample size is too small to draw a conclusion.

The alternative hypothesis is true.

The H0 is true.

The experiment was poorly designed.

When we fail to reject the null hypothesis, it means that there is not enough evidence to conclude that the null hypothesis is false. This does not imply that the null hypothesis is true, but rather that we do not have sufficient evidence to reject it.

Explanation

When we fail to reject the null hypothesis, it means that there is not enough evidence to conclude that the null hypothesis is false. This does not imply that the null hypothesis is true, but rather that we do not have sufficient evidence to reject it.

5. What range of values is the result of chance/probability generally between?

1 and 2

Greater than 1

Less than 0

Between 0 and 1

The result of chance/probability is typically between 0 and 1 as it represents the likelihood of an event occurring with 0 meaning impossible and 1 meaning certain.

Explanation

The result of chance/probability is typically between 0 and 1 as it represents the likelihood of an event occurring with 0 meaning impossible and 1 meaning certain.

6. What is considered unlikely to be chance evidence for the alternative hypothesis H1 and warrants rejecting the null hypothesis or indicating a real difference?

Small P's less than or equal to 0.05 or 5%

P's exactly equal to 0.05 or 5%.

P-values can be disregarded when testing alternative hypotheses.

Large P's greater than or equal to 0.05 or 5%.

In hypothesis testing, a small p-value (typically less than or equal to 0.05) indicates strong evidence against the null hypothesis and supports the alternative hypothesis, suggesting a real difference or effect.

Explanation

In hypothesis testing, a small p-value (typically less than or equal to 0.05) indicates strong evidence against the null hypothesis and supports the alternative hypothesis, suggesting a real difference or effect.

7. What is a Type 1 error in hypothesis testing?

True positive - Correctly rejecting the null hypothesis when it is true.

False negative - Falsely confirming the null hypothesis when it is false.

True negative - Not detecting a real effect that is present.

False positive -- A false alarm - Rejecting the null hypothesis when it is true

True false - Accepting the null hypothesis when it is false.

A Type 1 error, also known as a false positive, occurs when the null hypothesis is wrongly rejected even though it is true. This leads to a false alarm indicating a significant effect when there isn't one present.

Explanation

A Type 1 error, also known as a false positive, occurs when the null hypothesis is wrongly rejected even though it is true. This leads to a false alarm indicating a significant effect when there isn't one present.

8. What does NULL HYPOTHESIS stand for in research?

A hypothesis with a clearly defined outcome

A hypothesis that is always proven to be true

A hypothesis that assumes there is a significant difference between exposure and outcome

Start off with this. There is no chance, no difference between exposure and outcome eg. tobacco smoke and lung cancer.

The NULL HYPOTHESIS is a statement that assumes there is no difference or relationship between variables being examined in a research study.

Explanation

The NULL HYPOTHESIS is a statement that assumes there is no difference or relationship between variables being examined in a research study.

9. Is H1 true?

H1 is false.

I ACCEPT THE NULL HYPOTHESIS.

I REJECT THE NULL HYPOTHESIS

H1 is inconclusive.

In hypothesis testing, when we reject the null hypothesis (H0), it means that we have enough evidence to support the alternative hypothesis (H1). Therefore, the correct answer is 'I REJECT THE NULL HYPOTHESIS.'The other options are incorrect because they do not align with the concept of hypothesis testing.

Explanation

In hypothesis testing, when we reject the null hypothesis (H0), it means that we have enough evidence to support the alternative hypothesis (H1). Therefore, the correct answer is 'I REJECT THE NULL HYPOTHESIS.'The other options are incorrect because they do not align with the concept of hypothesis testing.

10. What is a Type II error?

Correctly rejecting the null hypothesis when it is true

Having a small sample size to detect potential associations

Accepting the null hypothesis when it should have been rejected (it is false)

A Type II error occurs when the null hypothesis is false, but the test fails to reject it. This leads to a missed opportunity to detect a different association if it actually exists. It is important to have a sufficient sample size to minimize the risk of committing a Type II error.

Explanation

A Type II error occurs when the null hypothesis is false, but the test fails to reject it. This leads to a missed opportunity to detect a different association if it actually exists. It is important to have a sufficient sample size to minimize the risk of committing a Type II error.