test_means_between: Test two means against each other for a "between-subjects"...
In darrellpenta/uxstats: Common data analysis tools for UX researchers

Description Usage Arguments Details Value See Also Examples

View source: R/test_means_between-function.R

test_means_between() compares two sample means obtained from two different groups of users and performs a two-sided statistical test to determine if the means differ from each other.

test_means_between, ab_test_between, and test_times_between are synonyms.

test_means_between(.x, ...)

ab_test_between(.x, ...)

## S3 method for class 'numeric'
test_means_between(.x, .sd, .n, .x2, .sd2, .n2, ..., .alpha = 0.05)

## S3 method for class 'data.frame'
test_means_between(.x, .var1, .var2, ..., .alpha = 0.05)

`.x`	A single numeric value (i.e., the mean from sample 1) or a data frame with named columns of numeric data corresponding to the variables being tested. See Details.
`...`	(Optional) If `.x` is a long-format data frame with a separate unique column of data for each sample, you can pass the name of one or more grouping variables as unquoted, comma-separated column names (without naming the `...` argument) to compute stats by groups.
`.sd`	If `.x` is a single numeric value representing the mean of the variable for sample 1, `.sd` should be a single numeric value representing the standard deviation of the variable for sample 1. See Details.
`.n`	If `.x` is a single numeric value representing the mean of the variable for sample 1, `.sd` should be a single numeric value representing the total number of users in sample 1. See Details.
`.x2`	If `.x` is a single numeric value representing the mean of the variable for sample 1,`.x2` should be a single numeric value representing the mean from sample 2.
`.sd2`	If `.x` is a single numeric value representing the mean of the variable for sample 1, `.sd2` should be a single numeric value representing the standard deviation of the variable for sample 2. See Details.
`.n2`	If `.x` is a single numeric value representing the mean of the variable for sample 1, `.sd` should be a single numeric value representing the total number of users in sample 2. See Details.
`.alpha`	(Optional) A positive number (where 0 < `.alpha` < 1) specifying the significance level to be used. Defaults to `.alpha = 0.05`. To set a different significance level, the argument must be named (i.e., `.alpha = 0.001`) or else the function may yield unexpected results.
`.var1`	If `.x` is a long-format data frame, the (unquoted) name of a data frame column containing the values representing the first sample.
`.var2`	If `.x` is a long-format data frame, the (unquoted) name of a data frame column containing the values representing the second sample.

If .x is a single numeric value representing the mean of the the variable for sample 1, use .sd and .n to provide the standard deviation and sample size. Use .x2,.sd2,.n2 to provide the equivalent information for sample 2.
If .x is a data frame with unique variable columns for each sample,.var1 and .var2 should be the unquoted names of the columns containing the values for sample 1 and sample 2
If .x is a data frame with only one variable column for both samples, pass the unquoted name of the column containing the values to compute means over to .var1, set .var2 = NULL, and use the grouping argument (...) to provide the name of the column that contains the sample id, which should only contain two unique values. Note that if you use this option, you cannot pass other grouping variables.
You can modify the alpha level to adjust confidence intervals by including .alpha as a named argument and providing a numeric value: e.g., .aplha = 0.001.
If you're passing a data frame to .x, you can optionally pass one or more grouping variables as unquoted, comma-separated column names (without naming the ... argument) to compute stats by groups.

Note that NAs are automatically dropped in all calculations.

A tibble with the results of a test of two means, with confidence interval information, and other information.

Other tests comparing means: test_means_within()

# When you already have summary stats:

test_means_between(.x = 45,.sd = 9.2,.n = 18,.x2 = 48, .sd2 = 8.5, .n2 = 17, .alpha = 0.01)


# If you have a data frame with a separate column of observations
# for each sample:

.ux_data<-
  data.frame("sample1_ratings" = runif(20,20,50),
             "sample2_ratings" = c(runif(15,20,50),rep(NA,times=5)))

test_means_between(.x = .ux_data, .var1 = sample1_ratings, .var2 = sample2_ratings, .alpha = 0.10)