TESTING OF HYPOTHESIS – PARAMETIRC TESTS

What is hypothesis testing?

• A technique for collecting data from a population

• A method used to prove a theory without any evidence

• A process for estimating the mean of a sample

• A statistical method used to make inferences about a population parameter based on sample data.

What are parametric tests?

• Parametric tests are only used for small sample sizes

• Parametric tests do not require any assumptions about the population parameters

• Parametric tests are used for non-normal distributions

• Parametric tests are statistical tests that make certain assumptions about the population parameters.

Give an example of a parametric test.

• Mann-Whitney U test

• t-test

• ANOVA

• Chi-square test

What is the purpose of hypothesis testing?

• To make inferences about a population parameter based on sample data.

• To guess randomly about the population parameter

• To make assumptions without any evidence

• To prove that the sample data is always accurate

How do we determine the significance level in hypothesis testing?

• By asking a magic eight ball

• By setting it at a predetermined value, such as 0.05

• By consulting a horoscope

• By flipping a coin

What are the steps involved in hypothesis testing?

• Collecting data, choosing test statistic, determining significance level, calculating p-value, making decision, interpreting results

• Formulating null and alternative hypotheses, choosing test statistic, determining significance level, calculating test statistic, making decision, interpreting results

• Selecting sample, conducting survey, analyzing data, drawing conclusion, presenting findings

• Formulating hypothesis, conducting experiment, analyzing data, drawing conclusion, publishing results

Explain the concept of Type I error in hypothesis testing.

• Type I error occurs when the alternative hypothesis is rejected when it is actually true.

• Type I error occurs when the null hypothesis is accepted when it is actually false.

• Type I error occurs when the null hypothesis is rejected when it is actually true.

• Type I error occurs when the sample size is too small to make a conclusion.

What is the difference between one-tailed and two-tailed tests?

• One-tailed tests focus on one direction of the distribution, while two-tailed tests consider both directions.

• One-tailed tests focus on two directions of the distribution, while two-tailed tests consider only one direction.

• One-tailed tests are used for non-parametric tests, while two-tailed tests are used for parametric tests.

• One-tailed tests are used for small sample sizes, while two-tailed tests are used for large sample sizes.

Why is it important to choose the right parametric test for hypothesis testing?

• It doesn't matter which test is chosen

• To make the results more confusing

• To waste time and resources

• To ensure accurate and reliable results