TESTING OF HYPOTHESIS

What is a sampling distribution?

• A sampling distribution is the distribution of a statistic obtained from multiple samples of a population.

• A sampling distribution is the total number of samples taken from a population.

• A sampling distribution is the range of values in a population.

• A sampling distribution is the average of a single sample from a population.

What do we test when we look at a single mean?

• We test if the sample mean is significantly different from a hypothesized population mean.

• We test if the sample mean is greater than the population mean.

• We test the variance of the sample data.

• We test if the sample mean is equal to the population mean.

How do we find the proportion in a sample?

• Proportion = (Total number of observations) / (Number of favorable outcomes)

• Proportion = (Number of observations) - (Number of favorable outcomes)

• Proportion = (Number of favorable outcomes) + (Total number of observations)

• Proportion = (Number of favorable outcomes) / (Total number of observations)

What is a variance test used for?

• To assess the correlation between two variables.

• To measure the mean of different groups.

• To determine the mode of a dataset.

• A variance test is used to compare the variances of different groups.

What does the Chi-square test check for?

• The Chi-square test checks for the association or independence between categorical variables.

• The Chi-square test checks for the variance of a single categorical variable.

• The Chi-square test checks for the mean of numerical data.

• The Chi-square test checks for the correlation between two continuous variables.

What does it mean if two attributes are independent?

• Two attributes are independent if one attribute can predict the other.

• Two attributes are independent if the probability of their joint occurrence equals the product of their individual probabilities.

• Two attributes are independent if they have no correlation whatsoever.

• Two attributes are independent if they are always equal in value.

Can you explain what a large sample is?

• A large sample is any sample that exceeds 50 observations.

• A large sample is defined as a sample size of 10 or more observations.

• A large sample is generally defined as a sample size of 30 or more observations.

• A large sample is a sample size of 20 or more observations.

What is a small sample in statistics?

• A small sample is always representative of the population.

• A small sample in statistics is a subset of a population that is limited in size, often leading to less reliable results.

• A small sample is larger than a large sample.

• A small sample is a complete population.

How do we test for equality of variances?

• Use Levene's test or Bartlett's test.

• Apply a t-test for independent samples.

• Conduct a chi-square test for independence.

• Use ANOVA to compare means.

What is the purpose of a goodness of fit test?

• To predict future data points based on past trends.

• To assess how well observed data matches expected data under a specific hypothesis.

• To calculate the correlation between two variables.

• To determine the average of a dataset.