diff --git a/DESCRIPTION b/DESCRIPTION
index 55425d6d..f95c6464 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
-Type: Package
 Package: report
+Type: Package
 Title: Automated Reporting of Results and Statistical Models
-Version: 0.5.7.11
+Version: 0.5.7.12
 Authors@R:
     c(person(given = "Dominique",
              family = "Makowski",
@@ -56,7 +56,7 @@ Depends:
     R (>= 3.6)
 Imports:
     bayestestR (>= 0.13.0),
-    effectsize (>= 0.8.5),
+    effectsize (>= 0.8.6.1),
     insight (>= 0.19.3.2),
     parameters (>= 0.20.2),
     performance (>= 0.10.2),
@@ -125,6 +125,7 @@ Collate:
     'report_effectsize.R'
     'report_htest_chi2.R'
     'report_htest_cor.R'
+    'report_htest_fisher.R'
     'report_htest_friedman.R'
     'report_htest_ttest.R'
     'report_htest_wilcox.R'
@@ -146,4 +147,8 @@ Collate:
 Roxygen: list(markdown = TRUE)
 Remotes: 
   easystats/insight,
-  easystats/effectsize
+  easystats/datawizard,
+  easystats/parameters,
+  easystats/effectsize,
+  easystats/bayestestR,
+  easystats/performance
diff --git a/NEWS.md b/NEWS.md
index 2a6ba0a8..f068abfd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -2,11 +2,11 @@
 
 Major Changes
 
-* This release changes the licensing model of `{see}` to an MIT license.
+* This release changes the licensing model of `{see}` to an MIT license. 
 
 Minor changes 
 
-* `report` now supports variables of class `htest` for the Friedman test.
+* `report` now supports variables of class `htest` for the Chi2, Friedman test, and Fisher's exact test.
 
 * `report` now supports variables of class `Date`, treating them like factors.
 
diff --git a/R/report.htest.R b/R/report.htest.R
index adb28a34..9a13f9a8 100644
--- a/R/report.htest.R
+++ b/R/report.htest.R
@@ -74,7 +74,11 @@ report_effectsize.htest <- function(x, ...) {
   ## For Chi2 ---------------
 
   if (model_info$is_chi2test) {
-    out <- .report_effectsize_chi2(x, table, dot_args)
+    if (chi2_type(x) == "fisher") {
+      out <- .report_effectsize_fisher(x, table, dot_args)
+    } else {
+      out <- .report_effectsize_chi2(x, table, dot_args)
+    }
   }
 
   # TODO: Chi-squared test -------------
@@ -161,7 +165,10 @@ report_statistics.htest <- function(x, table = NULL, ...) {
   text <- NULL
 
   # Estimate
-  candidates <- c("rho", "r", "tau", "Difference", "r_rank_biserial", "Chi2")
+  candidates <- c(
+    "rho", "r", "tau", "Difference", "r_rank_biserial",
+    "Chi2", "Odds Ratio"
+  )
   estimate <- candidates[candidates %in% names(table)][1]
   if (!is.null(estimate) && !is.na(estimate)) {
     text <- paste0(tolower(estimate), " = ", insight::format_value(table[[estimate]]))
@@ -257,7 +264,11 @@ report_parameters.htest <- function(x, table = NULL, ...) {
     out <- .report_parameters_friedman(table, stats, effsize, ...)
     # chi2
   } else if (model_info$is_chi2test) {
-    out <- .report_parameters_chi2(table, stats, effsize, ...)
+    if (chi2_type(x) == "fisher") {
+      out <- .report_parameters_fisher(table, stats, effsize, ...)
+    } else {
+      out <- .report_parameters_chi2(table, stats, effsize, ...)
+    }
   } else {
     # TODO: default, same as t-test?
     out <- .report_parameters_htest_default(table, stats, effsize, ...)
@@ -312,7 +323,11 @@ report_model.htest <- function(x, table = NULL, ...) {
   }
 
   if (model_info$is_chi2test) {
-    text <- .report_model_chi2(x, table)
+    if (chi2_type(x) == "fisher") {
+      text <- .report_model_fisher(x, table)
+    } else {
+      text <- .report_model_chi2(x, table)
+    }
   }
 
   as.report_model(text, summary = text)
diff --git a/R/report_htest_fisher.R b/R/report_htest_fisher.R
new file mode 100644
index 00000000..dbfc1e98
--- /dev/null
+++ b/R/report_htest_fisher.R
@@ -0,0 +1,111 @@
+# report_table -----------------
+
+.report_table_fisher <- function(table_full, effsize) {
+  table_full <- cbind(table_full, attributes(effsize)$table)
+  list(table = NULL, table_full = table_full)
+}
+
+
+# report_effectsize ---------------------
+
+.report_effectsize_fisher <- function(x, table, dot_args, rules = "funder2019") {
+  args <- c(list(x), dot_args)
+  table <- do.call(effectsize::effectsize, args)
+  ci <- attributes(table)$ci
+  estimate <- names(table)[1]
+  rules <- ifelse(is.null(dot_args$rules), rules, dot_args$rules)
+
+  args <- list(table, rules = rules, dot_args)
+  interpretation <- do.call(effectsize::interpret, args)$Interpretation
+  rules <- .text_effectsize(attr(attr(interpretation, "rules"), "rule_name"))
+
+  if (estimate == "Cramers_v_adjusted") {
+    main <- paste0("Adjusted Cramer's v = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Tschuprows_t") {
+    main <- paste0("Tschuprow's t = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Tschuprows_t_adjusted") {
+    main <- paste0("Adjusted Tschuprow's t = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Pearsons_c") {
+    main <- paste0("Pearson's c = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "phi_adjusted") {
+    main <- paste0("Adjusted Phi = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Cohens_h") {
+    main <- paste0("Cohen's h = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Odds_ratio") {
+    main <- paste0("Odds ratio = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "Ris_kratio") {
+    main <- paste0("Risk ratio = ", insight::format_value(table[[estimate]]))
+  } else if (estimate == "cohens_h") {
+    main <- paste0("Cohen's w = ", insight::format_value(table[[estimate]]))
+  } else {
+    main <- paste0(estimate, " = ", insight::format_value(table[[estimate]]))
+  }
+
+  statistics <- paste0(
+    main,
+    ", ",
+    insight::format_ci(table$CI_low, table$CI_high, ci)
+  )
+
+  table <- datawizard::data_rename(
+    as.data.frame(table),
+    c("CI_low", "CI_high"),
+    paste0(estimate, c("_CI_low", "_CI_high"))
+  )
+
+  table <- table[c(estimate, paste0(estimate, c("_CI_low", "_CI_high")))]
+
+  list(
+    table = table, statistics = statistics, interpretation = interpretation,
+    rules = rules, ci = ci, main = main
+  )
+}
+
+# report_model ----------------------------
+
+.report_model_fisher <- function(x, table) {
+  vars_full <- paste0(names(attributes(x$observed)$dimnames), collapse = " and ")
+
+  text <- paste0(
+    trimws(x$method),
+    " testing the association between the variables of the ",
+    x$data.name, " dataset "
+  )
+
+  text
+}
+
+chi2_type <- function(x) {
+  if (grepl("probabilities", x$method, fixed = TRUE)) {
+    out <- "probabilities"
+  } else if (grepl("Pearson", x$method, fixed = TRUE)) {
+    out <- "pearson"
+  } else if (grepl("Fisher", x$method, fixed = TRUE)) {
+    out <- "fisher"
+  }
+  out
+}
+
+.report_parameters_fisher <- function(table, stats, effsize, ...) {
+  text_full <- paste0(
+    "statistically ",
+    effectsize::interpret_p(table$p, rules = "default"),
+    ", and ",
+    attributes(effsize)$interpretation,
+    " (",
+    stats,
+    ")"
+  )
+
+  text_short <- paste0(
+    "statistically ",
+    effectsize::interpret_p(table$p, rules = "default"),
+    ", and ",
+    attributes(effsize)$interpretation,
+    " (",
+    summary(stats),
+    ")"
+  )
+
+  list(text_short = text_short, text_full = text_full)
+}
diff --git a/tests/testthat/_snaps/windows/report.brmsfit.md b/tests/testthat/_snaps/windows/report.brmsfit.md
index 057cabee..6f1f6348 100644
--- a/tests/testthat/_snaps/windows/report.brmsfit.md
+++ b/tests/testthat/_snaps/windows/report.brmsfit.md
@@ -2,7 +2,7 @@
 
     Code
       report(model, verbose = FALSE)
-    Message <simpleMessage>
+    Message
       Start sampling
     Output
       We fitted a Bayesian linear model (estimated using MCMC sampling with 4 chains
@@ -30,13 +30,14 @@
       (Highest Density Interval), along the probability of direction (pd), the
       probability of significance and the probability of being large. The thresholds
       beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81|. Convergence and stability of the Bayesian sampling
-      has been assessed using R-hat, which should be below 1.01 (Vehtari et al.,
-      2019), and Effective Sample Size (ESS), which should be greater than 1000
-      (Burkner, 2017)., We fitted a Bayesian linear model (estimated using MCMC
-      sampling with 4 chains of 300 iterations and a warmup of 150) to predict mpg
-      with qsec and wt (formula: mpg ~ qsec + wt). Priors over parameters were set as
-      uniform (location = , scale = ) distributions. The model's explanatory power is
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
@@ -58,13 +59,14 @@
       (Highest Density Interval), along the probability of direction (pd), the
       probability of significance and the probability of being large. The thresholds
       beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81|. Convergence and stability of the Bayesian sampling
-      has been assessed using R-hat, which should be below 1.01 (Vehtari et al.,
-      2019), and Effective Sample Size (ESS), which should be greater than 1000
-      (Burkner, 2017)., We fitted a Bayesian linear model (estimated using MCMC
-      sampling with 4 chains of 300 iterations and a warmup of 150) to predict mpg
-      with qsec and wt (formula: mpg ~ qsec + wt). Priors over parameters were set as
-      uniform (location = , scale = ) distributions. The model's explanatory power is
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner,
+      2017)., We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as uniform
+      (location = , scale = ) distributions. The model's explanatory power is
       substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
       model:
       
@@ -86,15 +88,16 @@
       (Highest Density Interval), along the probability of direction (pd), the
       probability of significance and the probability of being large. The thresholds
       beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81|. Convergence and stability of the Bayesian sampling
-      has been assessed using R-hat, which should be below 1.01 (Vehtari et al.,
-      2019), and Effective Sample Size (ESS), which should be greater than 1000
-      (Burkner, 2017). and We fitted a Bayesian linear model (estimated using MCMC
-      sampling with 4 chains of 300 iterations and a warmup of 150) to predict mpg
-      with qsec and wt (formula: mpg ~ qsec + wt). Priors over parameters were set as
-      student_t (location = 0.00, scale = 5.40) distributions. The model's
-      explanatory power is substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 =
-      0.79).  Within this model:
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
+      and We fitted a Bayesian linear model (estimated using MCMC sampling with 4
+      chains of 300 iterations and a warmup of 150) to predict mpg with qsec and wt
+      (formula: mpg ~ qsec + wt). Priors over parameters were set as student_t
+      (location = 0.00, scale = 5.40) distributions. The model's explanatory power is
+      substantial (R2 = 0.82, 95% CI [0.75, 0.85], adj. R2 = 0.79).  Within this
+      model:
       
         - The effect of b Intercept (Median = 19.74, 95% CI [9.45, 32.02]) has a 99.83%
       probability of being positive (> 0), 99.83% of being significant (> 0.30), and
@@ -114,8 +117,8 @@
       (Highest Density Interval), along the probability of direction (pd), the
       probability of significance and the probability of being large. The thresholds
       beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81|. Convergence and stability of the Bayesian sampling
-      has been assessed using R-hat, which should be below 1.01 (Vehtari et al.,
-      2019), and Effective Sample Size (ESS), which should be greater than 1000
-      (Burkner, 2017).
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
 
diff --git a/tests/testthat/_snaps/windows/report.htest-chi2.md b/tests/testthat/_snaps/windows/report.htest-chi2.md
index 618bb13b..937bddf6 100644
--- a/tests/testthat/_snaps/windows/report.htest-chi2.md
+++ b/tests/testthat/_snaps/windows/report.htest-chi2.md
@@ -1,138 +1,3 @@
-# report.htest-chi2 report_effectsize
-
-    Code
-      report_effectsize(x)
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, rules = "funder2019")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, rules = "gignac2016")
-    Output
-      Effect sizes were labelled following Gignac's (2016) recommendations. 
-      
-      small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, rules = "cohen1988")
-    Output
-      Effect sizes were labelled following Cohen's (1988) recommendations. 
-      
-      small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, rules = "evans1996")
-    Output
-      Effect sizes were labelled following Evans's (1996) recommendations. 
-      
-      very weak (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, rules = "lovakov2021")
-    Output
-      Effect sizes were labelled following Lovakov's (2021) recommendations. 
-      
-      very small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "cramers_v")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      small (Adjusted Cramer's v = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "pearsons_c")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      small (Pearson's c = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "tschuprows_t", adjust = FALSE)
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      very small (Tschuprow's t = 0.09, 95% CI [0.06, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "tschuprows_t")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      very small (Adjusted Tschuprow's t = 0.08, 95% CI [0.06, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "cohens_w")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      small (Cohens_w = 0.10, 95% CI [0.07, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "phi")
-    Output
-      Effect sizes were labelled following Funder's (2019) recommendations. 
-      
-      large (Adjusted Phi = 0.36, 95% CI [0.25, 1.00])
-
----
-
-    Code
-      report_effectsize(x, type = "cohens_h", rules = "sawilowsky2009")
-    Output
-      Effect sizes were labelled following Savilowsky's (2009) recommendations. 
-      
-      medium (Cohen's h = 0.74, 95% CI [0.50, 0.99])
-
----
-
-    Code
-      report_effectsize(x, type = "oddsratio", rules = "chen2010")
-    Output
-      Effect sizes were labelled following Chen's (2010) recommendations. 
-      
-      medium (Odds ratio = 4.73, 95% CI [2.74, 8.17])
-
----
-
-    Code
-      report_effectsize(x, type = "riskratio")
-    Output
-       
-      
-       (Risk_ratio = 2.54, 95% CI [1.80, 3.60])
-
 # report.htest-chi2 report
 
     Code
@@ -318,10 +183,10 @@
     Code
       report(x)
     Output
-      
+      Effect sizes were labelled following Funder's (2019) recommendations.
       
       The Chi-squared test for given probabilities / goodness of fit of
       table(mtcars$cyl) to a distribution of [4: n=3.2, 6: n=9.6, 8: n=19.2] suggests
-      that the effect is statistically significant, and (chi2 = 21.12, p < .001; Fei
-      = 0.27, 95% CI [0.17, 1.00])
+      that the effect is statistically significant, and medium (chi2 = 21.12, p <
+      .001; Fei = 0.27, 95% CI [0.17, 1.00])
 
diff --git a/tests/testthat/_snaps/windows/report.htest-correlation.md b/tests/testthat/_snaps/windows/report.htest-correlation.md
index ba684131..30968158 100644
--- a/tests/testthat/_snaps/windows/report.htest-correlation.md
+++ b/tests/testthat/_snaps/windows/report.htest-correlation.md
@@ -13,7 +13,8 @@
 
     Code
       report(cor.test(mtcars$wt, mtcars$mpg, method = "spearman"))
-    Warning <simpleWarning>
+    Condition
+      Warning in `cor.test.default()`:
       Cannot compute exact p-value with ties
     Output
       Effect sizes were labelled following Funder's (2019) recommendations.
@@ -26,7 +27,8 @@
 
     Code
       report(cor.test(mtcars$wt, mtcars$mpg, method = "kendall"))
-    Warning <simpleWarning>
+    Condition
+      Warning in `cor.test.default()`:
       Cannot compute exact p-value with ties
     Output
       Effect sizes were labelled following Funder's (2019) recommendations.
diff --git a/tests/testthat/_snaps/windows/report.htest-fisher.md b/tests/testthat/_snaps/windows/report.htest-fisher.md
new file mode 100644
index 00000000..c98a5440
--- /dev/null
+++ b/tests/testthat/_snaps/windows/report.htest-fisher.md
@@ -0,0 +1,12 @@
+# report.htest-fisher report
+
+    Code
+      report(x)
+    Output
+      Effect sizes were labelled following Funder's (2019) recommendations.
+      
+      The Fisher's Exact Test for Count Data testing the association between the
+      variables of the TeaTasting dataset suggests that the effect is statistically
+      not significant, and large (odds ratio = 6.41, 95% CI [0.31, Inf], p = 0.243;
+      Adjusted Cramer's v = 0.35, 95% CI [0.00, 1.00])
+
diff --git a/tests/testthat/_snaps/windows/report.ivreg.md b/tests/testthat/_snaps/windows/report.ivreg.md
index 69d5d0cc..96f08a42 100644
--- a/tests/testthat/_snaps/windows/report.ivreg.md
+++ b/tests/testthat/_snaps/windows/report.ivreg.md
@@ -2,7 +2,7 @@
 
     Code
       report(ivr)
-    Message <simpleMessage>
+    Message
       Formula contains log- or sqrt-terms.
         See help("standardize") for how such terms are standardized.
       Formula contains log- or sqrt-terms.
diff --git a/tests/testthat/_snaps/windows/report.lmer.md b/tests/testthat/_snaps/windows/report.lmer.md
index 2deadcfd..e9561fa2 100644
--- a/tests/testthat/_snaps/windows/report.lmer.md
+++ b/tests/testthat/_snaps/windows/report.lmer.md
@@ -23,11 +23,11 @@
 
     Code
       report(m2)
-    Message <simpleMessage>
+    Message
       boundary (singular) fit: see help('isSingular')
     Output
       Random effect variances not available. Returned R2 does not account for random effects.
-    Message <simpleMessage>
+    Message
       boundary (singular) fit: see help('isSingular')
     Output
       Random effect variances not available. Returned R2 does not account for random effects.
diff --git a/tests/testthat/_snaps/windows/report.stanreg.md b/tests/testthat/_snaps/windows/report.stanreg.md
index 1e8837d9..42313b27 100644
--- a/tests/testthat/_snaps/windows/report.stanreg.md
+++ b/tests/testthat/_snaps/windows/report.stanreg.md
@@ -25,8 +25,8 @@
       (Highest Density Interval), along the probability of direction (pd), the
       probability of significance and the probability of being large. The thresholds
       beyond which the effect is considered as significant (i.e., non-negligible) and
-      large are |0.30| and |1.81|. Convergence and stability of the Bayesian sampling
-      has been assessed using R-hat, which should be below 1.01 (Vehtari et al.,
-      2019), and Effective Sample Size (ESS), which should be greater than 1000
-      (Burkner, 2017).
+      large are |0.30| and |1.81| (corresponding respectively to 0.05 and 0.30 of the
+      outcome's SD). Convergence and stability of the Bayesian sampling has been
+      assessed using R-hat, which should be below 1.01 (Vehtari et al., 2019), and
+      Effective Sample Size (ESS), which should be greater than 1000 (Burkner, 2017).
 
diff --git a/tests/testthat/test-report.htest-chi2.R b/tests/testthat/test-report.htest-chi2.R
index 9c401704..083234e2 100644
--- a/tests/testthat/test-report.htest-chi2.R
+++ b/tests/testthat/test-report.htest-chi2.R
@@ -1,98 +1,3 @@
-test_that("report.htest-chi2 report_effectsize", {
-  m <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
-  dimnames(m) <- list(gender = c("F", "M"), party = c("Democrat", "Independent", "Republican"))
-  x <- chisq.test(m)
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x)
-  )
-
-  # Rules
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, rules = "funder2019")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, rules = "gignac2016")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, rules = "cohen1988")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, rules = "evans1996")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, rules = "lovakov2021")
-  )
-
-  # Types
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "cramers_v")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "pearsons_c")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "tschuprows_t", adjust = FALSE)
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "tschuprows_t")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "cohens_w")
-  )
-
-  # Change dataset for "Error: Phi is not appropriate for non-2x2 tables."
-  dat <- structure(
-    c(71, 50, 30, 100),
-    dim = c(2L, 2L), dimnames = list(
-      Diagnosis = c("Sick", "Recovered"),
-      Group = c("Treatment", "Control")
-    ),
-    class = "table"
-  )
-  x <- chisq.test(dat)
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "phi")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "cohens_h", rules = "sawilowsky2009")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "oddsratio", rules = "chen2010")
-  )
-
-  expect_snapshot(
-    variant = "windows",
-    report_effectsize(x, type = "riskratio")
-  ) # riskratio has no interpretation in effectsize
-  # Watch carefully in case effectsize adds support
-})
-
 test_that("report.htest-chi2 report", {
   m <- as.table(rbind(c(762, 327, 468), c(484, 239, 477)))
   dimnames(m) <- list(gender = c("F", "M"), party = c("Democrat", "Independent", "Republican"))
diff --git a/tests/testthat/test-report.htest-fisher.R b/tests/testthat/test-report.htest-fisher.R
new file mode 100644
index 00000000..6c6d1ff8
--- /dev/null
+++ b/tests/testthat/test-report.htest-fisher.R
@@ -0,0 +1,16 @@
+test_that("report.htest-fisher report", {
+  TeaTasting <<-
+    matrix(c(3, 1, 1, 3),
+      nrow = 2,
+      dimnames = list(
+        Guess = c("Milk", "Tea"),
+        Truth = c("Milk", "Tea")
+      )
+    )
+  x <- fisher.test(TeaTasting, alternative = "greater")
+
+  expect_snapshot(
+    variant = "windows",
+    report(x)
+  )
+})