People often rely on visual cues when interpreting media. This is true with this experiment; the independent variable is the visual cue for viewers, in this case, “Actor,” and the dependent variable is the judgment being influenced for viewers, in this case, “Calories.”

Actor, as a visual cue for viewers, works because the actor’s physique is a stereotype-based judgment made by the viewer, which works in favor of the advertisers. Seeing a “buff” actor eat a grilled chicken sandwich leads the viewer to infer that “healthy people” eat that particular sandwich. Seeing a “dad bod”- type actor eating a grilled chicken sandwich leads the viewer to infer the opposite. Depending on the ad’s purpose, the type of actor should be included. This is an inference they want viewers to make in this case, so they can think a specific way while watching the ad.

On average, viewers will estimate different calorie counts in the grilled chicken sandwich depending on whether the actor is presented as “dad bod” or “buff.” An association between the actor’s physique and assumptions about the sandwich presented is explained by stereotyping/trait inference, a process in which viewers use what they see to make assumptions, therefore inferring the food’s calorie content.

This study includes two variables. The first, Actor, is a categorical variable with two sections, “Dad bod” and “Buff”. This variable was used as the independent variable by researchers. The second, Calories, is a continuous variable measured as the number of calories participants reported consuming after viewing the specific advertisement. This was used as the dependent variable by researchers because it depends on the independent variable to elicit the response. To test whether the average assumed calorie count differed between the two actors’ physiques, an independent samples t-test was conducted. This test compared a continuous variable between two independent groups to determine whether the observed differences are due to chance or to a meaningful difference.

Descriptive Statistics
Summary statistics by group
Group N Mean Standard Deviation Minimum Maximum
Buff 50 584.26 128.03 316.00 827.00
Dad bod 50 656.28 141.25 358.00 946.00
Shapiro-Wilk Normality Test Results
Tests of normality within each group
Group W Statistic p-value
Buff 0.976 0.385
Dad bod 0.985 0.790
NOTE: If one or both of the p-values are less than .05 and if the groups are smaller than 40, the t-test’s normality assumption may be violated, and you should use the Wilcoxon rank-sum test results instead of the t-test results.
Wilcoxon Rank-Sum Test Results
Nonparametric comparison of group distributions
Group 1 Group 2 W Statistic p-value
Buff Dad bod 882.000 0.011
Independent Samples t-Test Results
Welch's t-test (unequal variances assumed)
Group 1 Group 2 Mean (Group 1) Mean (Group 2) t Statistic Degrees of Freedom p-value 95% CI (Lower) 95% CI (Upper)
Buff Dad bod 584.26 656.28 −2.671 97.069 0.009 −125.53 −18.51

Here is the R code that produced the plot.

# ------------------------------
# Setup
# ------------------------------
if (!require("dplyr")) install.packages("dplyr")
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("gt")) install.packages("gt")
if (!require("gtExtras")) install.packages("gtExtras")

library(dplyr)
library(ggplot2)
library(gt)
library(gtExtras)

options(scipen = 999)

# ------------------------------
# Load Data
# ------------------------------
mydata <- read.csv("SandwichAd.csv") #Edit: TYPE YOUR DATA FILE'S NAME

mydata <- mydata %>%
  mutate(
    DV = Calories, #Edit: TYPE YOUR DEPENDENT VARIABLE'S NAME
    IV = Actor  #Edit: TYPE YOUR INDEPENDENT VARIABLE'S NAME
  )

# ------------------------------
# Histograms of DV per IV group with group means
# ------------------------------

# Calculate group means
group_means <- mydata %>%
  group_by(IV) %>%
  summarise(
    mean_DV = mean(DV, na.rm = TRUE),
    .groups = "drop"
  )

Graphic <- ggplot(mydata, aes(x = DV)) +
  geom_histogram(
    binwidth = diff(range(mydata$DV, na.rm = TRUE)) / 30,
    color = "black",
    fill = "#1f78b4",
    alpha = 0.7
  ) +
  geom_vline(
    data = group_means,
    aes(xintercept = mean_DV),
    color = "red",
    linetype = "dashed",
    linewidth = 1
  ) +
  facet_grid(IV ~ .) +
  labs(
    title = "DV Distributions by IV Group",
    x = "Dependent Variable",
    y = "Count"
  ) +
  theme_minimal()

# Show graphic
Graphic

# ------------------------------
# Descriptive Statistics
# ------------------------------

Descriptives <- mydata %>%
  group_by(IV) %>%
  summarise(
    count = n(),
    mean = mean(DV, na.rm = TRUE),
    sd = sd(DV, na.rm = TRUE),
    min = min(DV, na.rm = TRUE),
    max = max(DV, na.rm = TRUE),
    .groups = "drop"
  )

DescriptivesTable <- Descriptives %>%
  gt() %>%
  fmt_number(
    columns = c(mean, sd, min, max),
    decimals = 2
  ) %>%
  tab_header(
    title = "Descriptive Statistics",
    subtitle = "Summary statistics by group"
  ) %>%
  cols_label(
    IV = "Group",
    count = "N",
    mean = "Mean",
    sd = "Standard Deviation",
    min = "Minimum",
    max = "Maximum"
  )

# ------------------------------
# Normality Check (Shapiro-Wilk)
# ------------------------------

shapiro_summary <- mydata %>%
  group_by(IV) %>%
  summarise(
    W_statistic = shapiro.test(DV)$statistic,
    p_value = shapiro.test(DV)$p.value,
    .groups = "drop"
  )

ShapiroTable <- shapiro_summary %>%
  gt() %>%
  fmt_number(
    columns = c(W_statistic, p_value),
    decimals = 3
  ) %>%
  tab_header(
    title = "Shapiro-Wilk Normality Test Results",
    subtitle = "Tests of normality within each group"
  ) %>%
  cols_label(
    IV = "Group",
    W_statistic = "W Statistic",
    p_value = "p-value"
  ) %>%
  tab_source_note(
    source_note = md(
      "NOTE: If one or both of the p-values are less than .05 and if the groups are smaller than 40, the t-test's normality assumption may be violated, and you should use the Wilcoxon rank-sum test results instead of the t-test results."
    )
  )

# ------------------------------
# Inferential Tests
# ------------------------------

# Run Welch's t-test (default for two groups)
t_res <- t.test(
  DV ~ IV,
  data = mydata,
  var.equal = FALSE
)

# Run Wilcoxon rank-sum test
wilcox_res <- wilcox.test(
  DV ~ IV,
  data = mydata
)

# Create a tidy summary of t-test results
t_summary <- tibble(
  Group1 = levels(as.factor(mydata$IV))[1],
  Group2 = levels(as.factor(mydata$IV))[2],
  Mean1 = t_res$estimate[1],
  Mean2 = t_res$estimate[2],
  t = t_res$statistic,
  df = t_res$parameter,
  p = t_res$p.value,
  CI_low = t_res$conf.int[1],
  CI_high = t_res$conf.int[2]
)

# Create a tidy summary of Wilcoxon results
wilcox_summary <- tibble(
  Group1 = levels(as.factor(mydata$IV))[1],
  Group2 = levels(as.factor(mydata$IV))[2],
  W = wilcox_res$statistic,
  p = wilcox_res$p.value
)

# ------------------------------
# Present t-Test Results as gt Table
# ------------------------------

Table <- t_summary %>%
  gt() %>%
  fmt_number(
    columns = c(Mean1, Mean2, CI_low, CI_high),
    decimals = 2
  ) %>%
  fmt_number(
    columns = c(t, df, p),
    decimals = 3
  ) %>%
  tab_header(
    title = "Independent Samples t-Test Results",
    subtitle = "Welch's t-test (unequal variances assumed)"
  ) %>%
  cols_label(
    Group1 = "Group 1",
    Group2 = "Group 2",
    Mean1 = "Mean (Group 1)",
    Mean2 = "Mean (Group 2)",
    t = "t Statistic",
    df = "Degrees of Freedom",
    p = "p-value",
    CI_low = "95% CI (Lower)",
    CI_high = "95% CI (Upper)"
  )

# ------------------------------
# Present Wilcoxon Results as gt Table
# ------------------------------

WilcoxonTable <- wilcox_summary %>%
  gt() %>%
  fmt_number(
    columns = c(W, p),
    decimals = 3
  ) %>%
  tab_header(
    title = "Wilcoxon Rank-Sum Test Results",
    subtitle = "Nonparametric comparison of group distributions"
  ) %>%
  cols_label(
    Group1 = "Group 1",
    Group2 = "Group 2",
    W = "W Statistic",
    p = "p-value"
  )

# ------------------------------
# Visual Output
# ------------------------------

# Histogram
Graphic

# Descriptive statistics table
DescriptivesTable

# Shapiro-Wilk table
ShapiroTable

# Wilcoxon rank-sum table
WilcoxonTable

# Welch's t-test table
Table