Lab 6: Hypothesis Testing - Two Independent Groups

NoteDigital Accessibility

Please note that all images were created with modifications to the defaults to make them digitally accessible. If you recreate this code in another environment, your plots have different colors and backgrounds.

1 Getting Started

Be sure to load the packages ggformula and mosaic, using the library() function. Remember, you need to do this with each new Quarto document or R Session. Add the package names in each of the blanks below to load in the indicated packages.

library() loads in packages. You need to supply the package name you need to load inside the parentheses.

library(ggformula) #for graphs library(mosaic) #for statistics library(tidyverse) #for data management

library(ggformula) #for graphs
library(mosaic) #for statistics
library(tidyverse) #for data management

TipRevisit Lab 5 Primer

The examples used in the Lab 6 Primer are continuations from the Lab 5 Primer. We encourage you to go back and review your previous answers and code to help you with your lab.

2 Sex Bias in Professor Ratings

Sex bias stems from a perceived mismatch from an expected role or characteristics based on sex. Studies have shown that men and women have unconscious sex biases against women in traditionally male-dominated fields (such as the sciences) or characteristics (such as leadership qualities). These biases often cause equally qualified women to be seen as less likable or less qualified than the men. (These links are to descriptions of two well-known studies, but there are plenty of other good resources).

Researchers are interested if this sex bias exists in traditionally female-dominated jobs as well, such as teaching. Students are asked to watch a video of an animated classroom and rate the professor. Each student is randomly assigned to either of two animations; the videos are exactly the same except for the sex of the professor drawn. You have been asked to analyze the data for the researchers to determine if the female-identifying professor is rated more poorly, on a 1 to 7 scale (with 7 being the best), than the male-identifying professor.

Run the following code chunk to read in the data and view the variable names and first 6 rows of the data.

2.1 Identify the Parameters

Identify the study design of this study.

Be sure you are able to provide a full justification. This is review from the Lab 5 Primer.

Independent Two Sample
Matched Pair Sample

Identify the parameter(s) that would be of interest based on the study design.

μₓₓ = true mean rating for female professor videos
μₓᵧ = true mean rating for male professor videos
μₓₓ₋ₓᵧ = true mean of the differences of the ratings between male and female professor videos
x̄ₓₓ = mean rating for our sample of female professor videos
x̄ₓᵧ = mean rating for our sample of male professor videos
x̄ₓₓ₋ₓᵧ = mean difference of the ratings between sample of male and female professor videos

Identify the null hypothesis that would be of interest based on the study design.

The researchers want to determine if the female-identifying professor is rated more poorly, on a 1 to 7 scale (with 7 being the best), than the male-identifying professor. (Female - Male)

μₓₓ - μₓᵧ = 0
μₓₓ - μₓᵧ < 0
μₓₓ - μₓᵧ > 0
μₓₓ - μₓᵧ ≠ 0
μₓₓ₋ₓᵧ = 0
μₓₓ₋ₓᵧ < 0
μₓₓ₋ₓᵧ > 0
μₓₓ₋ₓᵧ ≠ 0

Identify the alternative hypothesis that would be of interest based on the study design.

The researchers want to determine if the female-identifying professor is rated more poorly, on a 1 to 7 scale (with 7 being the best), than the male-identifying professor.

μₓₓ - μₓᵧ = 0
μₓₓ - μₓᵧ < 0
μₓₓ - μₓᵧ > 0
μₓₓ - μₓᵧ ≠ 0
μₓₓ₋ₓᵧ = 0
μₓₓ₋ₓᵧ < 0
μₓₓ₋ₓᵧ > 0
μₓₓ₋ₓᵧ ≠ 0

2.2 Exploratory Data Analysis

Recall from the Lab 5 Primer, we calculated the following summary statistics and data visualizations.

2.2.1 Summary Statistics

df_stats(Rating ~ Sex, data = bias)

	response	Sex	min	Q1	median	Q3	max	mean	sd	n	missing
1	Rating	Female	0	2.25	4	5.00	6	3.65	1.55	34	0
2	Rating	Male	3	4.00	5	5.75	7	4.76	1.02	34	0

2.2.2 Data Visualization

gf_boxplot(Rating ~ Sex, data = bias, 
        ylab = "Rating of Professor in Video (Scale 1-7)", 
        xlab = "Sex of the Professor in Video") 

2.2.3 QQ Plot

gf_qq(~Rating | Sex, data = bias,
      xlab = "Theoretical Z-Scores",
      ylab = "Rating of Professor in Video") |> 
  gf_qqline()

Based on the provided information, do we meet the necessary conditions to conduct inference using the t-distribution (e.g. confidence interval, hypothesis test)?

• Remember to check the conditions of sufficient sample size and normality together.
  
• The sufficient sample size depends on whether our sample indicates the population may or may not be normality distributed (now evaluated using the QQ Plot).
  
• Provide a statement, based on the condition check, to determine if we can or cannot use the t-distribution as a model for null distribution or sampling distribution of our test/sample statistic.

2.3 Calculating the Test Statistic and P-Value

We will practice code for both a “by-hand” calculation and using t.test() (which is what we will be using from in general).

For the “by-hand” calculation, we will need to split the dataset into two parts, one dataset for the male professor video ratings and one for the female professor video ratings.

We can use the function filter() to extract out specific rows associated with a specific variable value. Notice that we use double equal signs == to indicate equivalence with a particular value, and since our variable is a categorical (character), we put the value in quotes.

Calculate the summary statistics for each sample. Modify the code below to calculate the summary statistics for each group.

Here are the necessary summary statistics for the Female professor video Ratings. We will save them for later use. To get them to both save and print, we can add parentheses around each statement

2.4 Calculating a t-Test Statistic and p-Value

Now that we have the necessary summary statistics saved, we can calculate both our test statistic (t) and our p-value. Recall the t-statistic for an independent two-sample test is

$$t_0 = \frac{\bar{x}_1 - \bar{x}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$$

Recall that we are looking at Female Ratings - Male Ratings. Fill in the blanks below using the saved object names (e.g. mean_f, var_m) from above.

You should get $se = 0.3185897$ and $t = -3.508107$.

Now, calculate the p-value using the pt() function. Consider the direction of the alternative hypothesis.

pt(t, df = n_f - 1)

pt(t, df = n_f - 1)

Now, let’s calculate the test statistic and p-value using the t.test() function. Consider the direction of the alternative hypothesis. Recall you have three choices for the alternative.

• "two.sided"
  
• "greater"
  
• "less"

t.test(Rating ~ Sex, data = bias, mu = 0, alternative = "less")

t.test(Rating ~ Sex, data = bias, mu = 0, alternative = "less")

2.4.1 Switching the Direction of the Difference

Ultimately, it is up to the researcher to choose the direction of the calculated difference. If we wanted to switch our difference and have Male Ratings - Female Ratings, we would have to tell R to change the ordering of our variable using the mutate() function, since R defaults to reading our groups alphabetically and in the t.test() code would default to Female Ratings - Male Ratings (since F comes before M).

Here is the code to reorder the variable levels so "Male" is read first:

Now, rerun the t.test() code, but using bias_reorder. What do you have to change to make the test equivalent?

t.test(Rating ~ Sex, data = bias_reorder, mu = 0, alternative = "greater")

t.test(Rating ~ Sex, data = bias_reorder, mu = 0, alternative = "greater")

2.5 Interpreting and Evaluating the p-Value

Using the calculate p-value from the t.test() function to answer the following questions.

Reorder to provide the appropriate interpretation of the p-value.

There is a probability of

or less

that there is no difference between the true mean ratings for male and female professor videos

of observing our test statistic

0.0004447

of t = -3.5081

assuming the null hypothesis is true

Evaluate the strength of evidence against the null hypothesis, using a significance level of $\alpha = 0.05$

no evidence
little evidence
some evidence
moderate evidence
strong evidence
very strong evidence

NoteFull Evaluation of Strength of Evidence

Remember we have specific details to include in a full evaluation of the strength of evidence.

We have {very strong/strong/moderate/some/little} evidence against the null hypothesis (in favor of the alternative hypothesis) that {context of indicated hypothesis} (t = {xxx}, df = {xxx}, p-value = {xxx}, $\alpha$ = {xxx}).

Which of the following statements are true based on the p-value?

there is a very small probability the null is true
our data/test statistic is very unlikely to be observed if the null is true
there is a very small probability the alternative is true
there is a very large probability the alternative is true
there is a very small probability of finding evidence against the null

2.6 Calculating a Confidence Interval for the Difference of Two Means

In order to calculate the confidence interval for the difference of two population means, it takes on the same structure as a confidence interval for one population mean.

$$point \ estimate \pm (critical \ value)*(standard\ error)$$

or

$$\bar{x}_1 - \bar{x}_2 \ \pm \ t^*{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}$$

We can find our $t^*$ critical value the same way we found it for previous confidence intervals.

Our point estimate is $\bar{x}_1 - \bar{x}_2$

and finally we can calculate the lower bound (lb) and upper bound (ub) for our confidence interval.

Of course, the “by-hand” method is tedious when we can just use t.test() to calculate our confidence interval. Fill in the blanks below to calculate the 95% confidence interval for the difference between the two means.

t.test(Rating ~ Sex, data = bias, conf.level = 0.95)$conf.int

t.test(Rating ~ Sex, data = bias, conf.level = 0.95)$conf.int

Provide an interpretation of the confidence interval by reordering the phrases below.

within the interval

is a single value

the true mean rating of male professor videos

and

the difference between

LB and UB

the true mean rating of female professor videos

Based on the sample

we are 95% confident that

Which of the following would be plausible estimates for the difference between two true mean ratings?

0
-1
1
-1.5
-0.5
0.5
1.5
-2
2

What can we conclude about the study?

we can infer that the sex of the professor in the video causes ratings to be different for the videos
we cannot infer that the sex of the professor in the video causes ratings to be different for the videos
there is evidence the ratings for female professors are lower than for male professors in the videos
there is no evidence the ratings for female professors are lower than for male professors in the videos