Sampling
General Topics #1
General Topics #2
Statistical Tests #1
Statistical Tests #2
Interpretations
100

How are participants selected in convenience?

Participants are selected because they are conveniently located or accessible

100

What are the goals of statistical analysis?

• Summarize data

• Show basic patterns in the data

• Interpret patterns

• Generalize the patterns to the population

100

What is the first step of hypothesis testing?

Formulation of the null hypothesis and the alternative hypothesis

100

What influences our test results?

• Sample size

• Variance (dispersion of sample values)

• How certain we want to be about the conclusion

100

If you want to compare two variables for each respondent in the sample, which test should be used?

Paired Sample T-Test

100

What is the impact of a larger sample size?

Easier to find statistical significance

200

What is the first step of the sampling process?

Define the target population

200

What is the goal of inferential statistics?

• Use characteristics of the sample to estimate characteristics of the population

• In other words, use sample statistics to estimate population parameters

200

A false hypothesis that is not rejected is what Type of Error?

Type II Error

200

When should a One Sample T-test be used?

When we want to compare the mean of one sample to a set threshold

200

When testing the relationship between two variables, what tests should be used if our goal is to test difference in means?

• Paired sample t-test

• Independent sample t-test

200

Considering a binary “dummy” variable, variables are defined as 0 or 1. What would a 0 variable mean?

• 0 = condition is false.

• 1 = condition is true.

300

What are four examples of probability sampling?

Simple random sampling, systematic sampling, stratified sampling, cluster sampling

300

What are two examples of tests of associations?

• Correlation (interval and ratio)

• Proportions (ordinal and nominal) (Chi-Square tests)

300

What is the difference between inferential statistics and descriptive statistics?

• Inferential statistics: quantities from sample data calculated to make inferences about the population being studied

• Descriptive statistics: summary measures that describe your sample data

300

When should an Independent Sample T-Test be used?

When we want to compare the mean of one variable across two sub-groups in your population

300

When testing the relationship between two variables, what test should be used if our goal is to test associations with frequency distributions?

Bivariate Chi-Square Test

300

What does the R-squared value indicate?

The strength of association between X and Y

400

When is snowball sampling beneficial?

When populations are difficult to reach or identify

400

What is the difference between parameter and statistic?

• Parameter is a number describing the whole population

• Statistic is a number describing a sample

400

What is the correct interpretation of a p-value that is below alpha?

Rejection of the null hypothesis

400

What test should be used if we want to compare the frequency distribution of a sample variable to a hypothesized population distribution?

Chi-Square Test

400

What does an F-Test tell us?

The F-test of overall significance tells us whether our linear regression model provides a better fit to the data than a model that does not contain independent variables

400

According to this output, when “TR price in \$” increases by \$1, holding all else constant, what is the predicted increase in MM OJ sales?

+ 114.9

500

Define stratified sampling

1.Divide the population into subgroups (strata).

nthe strata don’t overlap and no one’s left out

2.Select an independent simple random sample from each stratum.

500

What are the two basic properties that we calculate for our sample?

• Central tendency (mean, median, mode)

• Dispersion/Spread (standard deviation, absolute frequencies, relative frequencies)

500

What are the two steps of the independent t-test?

• Hypothesis test of equality of variance of 2 samples

• Hypothesis test of difference of means

500

What are the assumptions of a Chi-Square Test?

• The expected counts (Ei) has to be greater than 5 for at least 80% of the cells in the table

• No cell can have an expected count below 1

• These assumptions check if there is enough data for the test to be accurate

500

When does the adjusted R-squared increase?

• When the new term improves the model more than it would be expected by change

• But, the adjusted R-squared is always lower than the R-squared

500

According to this output, when there is a Feature Ad that week, holding all else constant, what is the predicted increase in MM OJ sales?

+ 270.33