t-test in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a t-test on two numeric vectors x and y.

R Programming

result <- t.test([1], y)

Drag options to blanks, or click blank then click option'

Ax

Bmean

Ct.test

Dy

Attempts:

3 left

💡 Hint

Common Mistakes

Using the function name instead of a variable.

Passing the wrong variable as the first argument.

The t.test() function compares two numeric vectors. Here, x is the first sample.

2fill in blank

medium

Complete the code to perform a paired t-test on vectors a and b.

R Programming

t.test(a, b, paired = [1])

Drag options to blanks, or click blank then click option'

ANULL

BTRUE

CNA

DFALSE

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting to set paired = TRUE for paired tests.

Using paired = FALSE which runs an unpaired test.

Setting paired = TRUE tells R to perform a paired t-test, comparing matched samples.

3fill in blank

hard

Fix the error in the code to test if the mean of vector data is different from 5.

R Programming

t.test(data, mu = [1])

Drag options to blanks, or click blank then click option'

A5

BNULL

Cmean(data)

D"5"

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the number as a string with quotes.

Using the sample mean instead of the hypothesized mean.

The mu argument expects a numeric value, not a string. Use 5 without quotes.

4fill in blank

hard

Fill both blanks to perform a one-sided t-test checking if mean of x is greater than 10.

R Programming

t.test(x, mu = [1], alternative = [2])

Drag options to blanks, or click blank then click option'

A10

B"less"

C"greater"

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong alternative hypothesis string.

Setting mu to a wrong value.

Set mu = 10 as the hypothesized mean and alternative = "greater" to test if the true mean is greater than 10.

5fill in blank

hard

Fill all three blanks to perform a two-sample t-test assuming unequal variances between groups a and b.

R Programming

t.test([1], [2], var.equal = [3])

Drag options to blanks, or click blank then click option'

Aa

Bb

CFALSE

DTRUE

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the sample vectors.

Setting var.equal to TRUE when variances are unequal.

Use vectors a and b as samples, and set var.equal = FALSE to assume unequal variances (Welch's t-test).

Practice

1.

What does a t-test primarily compare in two groups?

easy

A. The difference between their medians

B. The difference between their averages

C. The total number of observations

D. The variance of each group

Solution

1. Step 1: Understand the purpose of a t-test

   A t-test is used to compare the means (averages) of two groups to see if they are different.

2. Step 2: Identify what is compared

   The test focuses on the average values, not medians, counts, or variances.

3. Final Answer:

   The difference between their averages -> Option B

4. Quick Check:

   t-test compares means = A [OK]

Hint: Remember: t-test checks averages, not medians or counts [OK]

Common Mistakes:

• Confusing mean with median
• Thinking it compares counts
• Assuming it compares variances

2.

Which of the following is the correct way to run a t-test in R comparing two numeric vectors x and y?

easy

A. t_test(x, y)

B. ttest(x, y)

C. t.test(x, y)

D. test.t(x, y)

Solution

1. Step 1: Recall the correct function name in R

   The built-in function for t-test in R is t.test().

2. Step 2: Check the syntax for comparing two vectors

   Using t.test(x, y) compares the two numeric vectors correctly.

3. Final Answer:

   t.test(x, y) -> Option C

4. Quick Check:

   Correct function is t.test() = A [OK]

Hint: Use exact function name t.test() with dot [OK]

Common Mistakes:

• Misspelling the function name
• Using underscores instead of dots
• Confusing function with other test names

3.

What will be the output of this R code snippet?

group1 <- c(5, 7, 8, 6, 9)
group2 <- c(10, 12, 11, 13, 14)
test <- t.test(group1, group2)
round(test$p.value, 3)

medium

A. 0.001

B. 0.05

C. 0.5

D. 1.0

Solution

1. Step 1: Understand the data groups

   Group1 has smaller values around 5-9, Group2 has larger values around 10-14, so means differ.

2. Step 2: Interpret the p-value from t.test()

   Since the groups differ clearly, the p-value will be very small, close to 0.001 after rounding.

3. Final Answer:

   0.001 -> Option A

4. Quick Check:

   Distinct groups give low p-value = B [OK]

Hint: Big difference in groups means very small p-value [OK]

Common Mistakes:

• Assuming p-value is large for different groups
• Confusing p-value with test statistic
• Ignoring rounding in output

4.

Identify the error in this R code for a t-test:

data1 <- c(4, 5, 6)
data2 <- c(7, 8, 9)
test <- t.test(data1 + data2)

medium

A. t.test() cannot compare numeric vectors

B. Missing parentheses in t.test()

C. Vectors data1 and data2 must be data frames

D. Using '+' instead of ',' to separate vectors

Solution

1. Step 1: Check the function call syntax

   The t.test() function expects two separate vectors as arguments separated by a comma.

2. Step 2: Identify the mistake in argument passing

   The code uses '+' to add vectors instead of separating them with a comma, which is incorrect.

3. Final Answer:

   Using '+' instead of ',' to separate vectors -> Option D

4. Quick Check:

   Arguments must be comma-separated = D [OK]

Hint: Separate vectors with comma, not plus sign [OK]

Common Mistakes:

• Adding vectors instead of passing separately
• Confusing function arguments syntax
• Thinking t.test needs data frames

5.

You have two groups of exam scores: before and after a training program. Which t-test is best to check if the training improved scores?

hard

A. Paired t-test using t.test(before, after, paired=TRUE)

B. Two-sample t-test using t.test(before, after)

C. One-sample t-test using t.test(after)

D. Variance test using var.test(before, after)

Solution

1. Step 1: Understand the data relationship

   Scores are from the same students before and after training, so data are paired.

2. Step 2: Choose the correct t-test type

   A paired t-test compares matched pairs to see if scores changed after training.

3. Final Answer:

   Paired t-test using t.test(before, after, paired=TRUE) -> Option A

4. Quick Check:

   Paired data needs paired=TRUE = C [OK]

Hint: Use paired=TRUE for before-after same subjects [OK]

Common Mistakes:

• Using two-sample test for paired data
• Using one-sample test incorrectly
• Confusing variance test with t-test