Basic Testing Hypothesis and Significance Level

What is a hypothesis in statistics?

• A hypothesis in statistics is a guess about a sample statistic.

• A hypothesis in statistics is a mathematical formula used to calculate probabilities.

• A hypothesis in statistics is a proven fact about a population parameter.

• A hypothesis in statistics is a statement or assumption about a population parameter.

Define the level of significance in hypothesis testing.

• The level of significance is the probability of rejecting the null hypothesis when it is actually true.

• The level of significance is the p-value of the hypothesis test.

• The level of significance is the confidence interval of the hypothesis test.

• The level of significance is the probability of accepting the null hypothesis when it is actually true.

What is the difference between a one-tailed test and a two-tailed test?

• The difference between a one-tailed test and a two-tailed test is the directionality of the hypothesis being tested.

• The sample size used in a one-tailed test is smaller than in a two-tailed test

• A one-tailed test is more accurate than a two-tailed test

• The results of a one-tailed test are always statistically significant

Explain the concept of Type I error in hypothesis testing.

• Type I error is the correct rejection of a false null hypothesis.

• Type I error is the failure to reject a true null hypothesis.

• Type I error is the incorrect rejection of a true null hypothesis.

• Type I error is the incorrect acceptance of a false null hypothesis.

When would you use a z-test instead of a t-test?

• When the sample size is large and the population standard deviation is unknown.

• When the sample size is large and the population standard deviation is known.

• When the sample size is small and the population standard deviation is known.

• When the sample size is small and the population standard deviation is unknown.

What is the formula for calculating the z-score in hypothesis testing?

• (X - μ) / (σ / √n)

• (X - μ) / (σ * n)

• (X - μ) * (σ / √n)

• (X - μ) / (σ * √n)

What is the critical value in hypothesis testing?

• The critical value is a point beyond which we reject the null hypothesis.

• The critical value is the p-value in hypothesis testing.

• The critical value is the point where we accept the null hypothesis.

• The critical value is always 0 in hypothesis testing.

How do you interpret the p-value in hypothesis testing?

• The p-value measures the effect size of the study

• The p-value determines the sample size needed for the study

• The p-value represents the probability of observing the data or more extreme results if the null hypothesis is true.

• The p-value indicates the strength of the relationship between variables

What is the null hypothesis?

• The null hypothesis states that there is no significant difference or relationship between specified populations or variables.

• The null hypothesis is always proven correct

• The null hypothesis is a prediction of the outcome

• The null hypothesis is only used in qualitative research

What is the alternative hypothesis?

• The alternative hypothesis is always proven to be true.

• The alternative hypothesis is a statement that contradicts the null hypothesis.

• The alternative hypothesis is not related to the null hypothesis.

• The alternative hypothesis is a statement that supports the null hypothesis.