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Two-sample t-test

A two-sample t-test is used to test if the means of two populations equal.

The examples of analyses that quantify the impact of a factor include testing a pharmaceutical drug on patients or a marketing campaign on demand.

Recall that few assumptions need to be met to carry out a two-sample t-test:

  • Random samples
  • Independent observations
  • Normally distributed underlying data
  • Homogeneity of variances

The former two assumptions need to be met at the stage of designing the experiment. The latter two assumptions can be tested using the Shapiro-Wilk test and Bartlett's test respectively.

A company provided you with the df data frame. The sample column indicates the sample, and the value column contains numerical data. The dplyr package is available in your environment.

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Practicing Statistics Interview Questions in R

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# Return the first part of df
head(___)

# Test normality of sample 1
sample1 <- df %>% filter(sample == ___) %>% select(value) %>% pull()
shapiro.test(___)

# Test normality of sample 2
sample2 <- df %>% filter(sample == 2) %>% select(___) %>% pull()
shapiro.test(___)
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