Statistical tests in R

Question 1

What command is used in R to conduct an independent samples t-test?

Answer 1

t.test

The t.test function is used to compare means between two groups.

Question 2

What is the null hypothesis in a two-sample test comparing means?

Answer 2

There will be no significant difference in the means

This hypothesis assumes that any observed difference is due to sampling error.

Question 3

What assumptions must be checked before conducting an independent samples t-test?

Answer 3

• The scatter of points around the means is roughly equal
• The scatter of points around the means follows a normal distribution

Question 4

What R command checks for equal variance?

Answer 4

var.test

This command tests whether two samples have equal variances.

Question 5

What is the alternative hypothesis in a two-sample test comparing means?

Answer 5

There will be a significant difference in the means

Question 6

What is the R command for the Mann-Whitney U test?

Answer 6

wilcox.test

Used for comparing the medians of two groups when the distribution is not normal.

Question 7

What is the primary purpose of the paired samples t-test?

Answer 7

To compare means of two paired samples

Question 8

What is the command for a one-way ANOVA in R?

Answer 8

aov

One-way ANOVA is used to compare means across three or more groups.

Question 9

What is the null hypothesis in a one-way ANOVA?

Answer 9

There will be no significant difference in the means among the groups

Question 10

What command is used to conduct a Kruskal-Wallis test in R?

Answer 10

kruskal.test

Question 11

What statistical test is used to analyze the relationship between two numerical variables?

Answer 11

Linear regression

Question 12

What is the R command for performing linear regression?

Answer 12

lm

The lm function fits a linear model.

Question 13

What does a correlation coefficient range from?

Answer 13

-1 to 1

Question 14

What are the assumptions for performing Pearson’s correlation?

Answer 14

• Linear relationship between variables
• Both variables have normal distributions

Question 15

What command is used to perform a Pearson’s correlation in R?

Answer 15

cor.test(VARIABLE A, VARIABLE B, method = ‘pearson’)

Question 16

What is the purpose of a Chi-squared contingency table test?

Answer 16

To test the association between two categorical variables

Question 17

What command is used in R for the Chi-squared test?

Answer 17

chisq.test(x)

Question 18

What is the alternative hypothesis in a test of association between two categorical variables?

Answer 18

There is a significant association between the variables

Question 19

What is the significance level often used in hypothesis testing?

Answer 19

0.05

Question 20

What is the Bonferroni correction used for?

Answer 20

To adjust the significance level when multiple tests are conducted

Question 21

What does the p-value indicate in hypothesis testing?

Answer 21

The probability of observing the data if the null hypothesis is true

Question 22

What is the command to check for normal distribution in R?

Answer 22

hist(model$residuals)

Question 23

What are the three different surgical techniques being compared for follow-up pain?

Answer 23

Technique A, Technique B, Technique C

Specific techniques are not detailed in the text.

Question 24

Is socio-economic status (SES) associated with the severity of COVID-19 infection?

Answer 24

Yes

SES is often linked to health outcomes.

Question 25

Does the frequency of growth inhibition of bacteria depend on the bacterial strain and the antibiotic used?

Answer 25

Yes ## Footnote Different strains may respond variably to antibiotics.

Question 26

What is the test statistic used in the Pearson's Chi-squared test?

Answer 26

χ2 ## Footnote This statistic compares observed and expected values.

Question 27

What are the assumptions for the Chi-squared test?

Answer 27

None of the expected values should be too small (<5) ## Footnote Ensures validity of the test results.

Question 28

What is a key feature of R in terms of flexibility?

Answer 28

R is insanely flexible ## Footnote Allows for extensive customization.

Question 29

What type of graphs can R produce?

Answer 29

Publication-quality graphs ## Footnote Fully customizable for specific needs.

Question 30

What is one benefit of R's modularity?

Answer 30

Analyses can be automated easily ## Footnote Streamlines repetitive tasks.

Question 31

What is R's stance on cost?

Answer 31

R is free ## Footnote Open-source software accessible to everyone.

Question 32

What is a common source of error in R coding?

Answer 32

Wrong case (R is case sensitive) ## Footnote Case sensitivity can lead to errors.

Question 33

What are some statistical tests you have the code for?

Answer 33

* t-tests (independent samples and paired samples) * One-way ANOVA * Linear regression * Correlation (Pearson’s and Spearman’s) * Chi-squared test ## Footnote These tests are fundamental for data analysis.

Question 34

What should be tested for in t-tests and ANOVA?

Answer 34

Equal variance ## Footnote Ensures valid comparisons between groups.

Question 35

What is an alternative to ANOVA if residuals are non-normal?

Answer 35

Kruskal-Wallis test ## Footnote Non-parametric alternative to ANOVA.

Question 36

Fill in the blank: R is _______.

Answer 36

fun ## Footnote Many users find R enjoyable to work with.

Question 37

What is the command to create a boxplot in ggplot?

Answer 37

ggplot(prac1B, aes(x=rainfall, y=phosphate)) + geom_boxplot() ## Footnote This command visualizes the relationship between rainfall and phosphate.