diff --git a/__tests__/Distributions__Test.re b/__tests__/Distributions__Test.re
index c2cfd026..aed93c8c 100644
--- a/__tests__/Distributions__Test.re
+++ b/__tests__/Distributions__Test.re
@@ -13,16 +13,15 @@ let makeTest = (~only=false, str, item1, item2) =>
       );
 
 let makeTestCloseEquality = (~only=false, str, item1, item2, ~digits) =>
-only
-  ? Only.test(str, () =>
-      expect(item1) |> toBeSoCloseTo(item2, ~digits)
-    )
-  : test(str, () =>
-      expect(item1) |> toBeSoCloseTo(item2, ~digits)
-    );
+  only
+    ? Only.test(str, () =>
+        expect(item1) |> toBeSoCloseTo(item2, ~digits)
+      )
+    : test(str, () =>
+        expect(item1) |> toBeSoCloseTo(item2, ~digits)
+      );
 
 describe("Shape", () => {
-  
   describe("Continuous", () => {
     open Distributions.Continuous;
     let continuous = make(`Linear, shape);
@@ -129,7 +128,7 @@ describe("Shape", () => {
       1.0,
     );
   });
-  
+
   describe("Discrete", () => {
     open Distributions.Discrete;
     let shape: DistTypes.xyShape = {
@@ -195,7 +194,13 @@ describe("Shape", () => {
       0.9,
     );
     makeTest("integralEndY", T.Integral.sum(~cache=None, discrete), 1.0);
-
+    makeTest("mean", T.getMean(discrete), 3.9);
+    makeTestCloseEquality(
+      "variance",
+      T.getVariance(discrete),
+      5.89,
+      ~digits=7,
+    );
   });
 
   describe("Mixed", () => {
@@ -300,7 +305,6 @@ describe("Shape", () => {
         },
       ),
     );
-  
   });
 
   describe("Distplus", () => {
@@ -380,33 +384,36 @@ describe("Shape", () => {
     let stdev = 4.0;
     let variance = stdev ** 2.0;
     let numSamples = 10000;
-
     open Distributions.Shape;
-    let normal: SymbolicDist.dist = `Normal({ mean, stdev});
+    let normal: SymbolicDist.dist = `Normal({mean, stdev});
     let normalShape = SymbolicDist.GenericSimple.toShape(normal, numSamples);
     let lognormal = SymbolicDist.Lognormal.fromMeanAndStdev(mean, stdev);
-    let lognormalShape = SymbolicDist.GenericSimple.toShape(lognormal, numSamples);
+    let lognormalShape =
+      SymbolicDist.GenericSimple.toShape(lognormal, numSamples);
 
     makeTestCloseEquality(
       "Mean of a normal",
-      T.getMean(normalShape), 
-      mean, 
-      ~digits=2);
+      T.getMean(normalShape),
+      mean,
+      ~digits=2,
+    );
     makeTestCloseEquality(
-      "Variance of a normal", 
-      T.getVariance(normalShape), 
-      variance, 
-      ~digits=1);
+      "Variance of a normal",
+      T.getVariance(normalShape),
+      variance,
+      ~digits=1,
+    );
     makeTestCloseEquality(
-      "Mean of a lognormal", 
-      T.getMean(lognormalShape), 
-      mean, 
-      ~digits=2);
+      "Mean of a lognormal",
+      T.getMean(lognormalShape),
+      mean,
+      ~digits=2,
+    );
     makeTestCloseEquality(
-      "Variance of a lognormal", 
-      T.getVariance(lognormalShape), 
-      variance, 
-      ~digits=0);
+      "Variance of a lognormal",
+      T.getVariance(lognormalShape),
+      variance,
+      ~digits=0,
+    );
   });
-
-});
+});
\ No newline at end of file
diff --git a/src/distPlus/distribution/Distributions.re b/src/distPlus/distribution/Distributions.re
index bf07336f..a97132d7 100644
--- a/src/distPlus/distribution/Distributions.re
+++ b/src/distPlus/distribution/Distributions.re
@@ -104,7 +104,7 @@ module Continuous = {
         )
         |> DistTypes.MixedPoint.makeContinuous;
       };
-    
+
       // let combineWithFn = (t1: t, t2: t, fn: (float, float) => float) => {
       //   switch(t1, t2){
       //     | ({interpolation: `Stepwise}, {interpolation: `Stepwise}) => 3.0
@@ -141,8 +141,22 @@ module Continuous = {
       let toScaledContinuous = t => Some(t);
       let toScaledDiscrete = _ => None;
 
-      let getMean = (t: t) => XYShape.Analysis.integrateContinuousShape(t);
-      let getVariance = (t: t): float => XYShape.Analysis.getVarianceDangerously(t, getMean, XYShape.Analysis.getMeanOfSquaresContinuousShape);
+      let getMean = (t: t) => {
+        let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 2.0 /. 2.0;
+        let indefiniteIntegralLinear = (p, a, b) =>
+          a *. p ** 2.0 /. 2.0 +. b *. p ** 3.0 /. 3.0;
+        XYShape.Analysis.integrateContinuousShape(
+          ~indefiniteIntegralStepwise,
+          ~indefiniteIntegralLinear,
+          t,
+        );
+      };
+      let getVariance = (t: t): float =>
+        XYShape.Analysis.getVarianceDangerously(
+          t,
+          getMean,
+          XYShape.Analysis.getMeanOfSquaresContinuousShape,
+        );
     });
 };
 
@@ -215,13 +229,14 @@ module Discrete = {
         |> Continuous.getShape
         |> XYShape.YtoX.linear(f);
 
-      let getMean = (t: t): float => E.A.reducei(t.xs, 0.0, (acc, x, i) => acc +. x*. t.ys[i]);
+      let getMean = (t: t): float =>
+        E.A.reducei(t.xs, 0.0, (acc, x, i) => acc +. x *. t.ys[i]);
       let getVariance = (t: t): float => {
-        let getMeanOfSquares = t => getMean(XYShape.Analysis.squareXYShape(t));
+        let getMeanOfSquares = t =>
+          getMean(XYShape.Analysis.squareXYShape(t));
         XYShape.Analysis.getVarianceDangerously(t, getMean, getMeanOfSquares);
       };
     });
-
 };
 
 // TODO: I think this shouldn't assume continuous/discrete are normalized to 1.0, and thus should not need the discreteProbabilityMassFraction being separate.
@@ -393,27 +408,38 @@ module Mixed = {
         };
       };
 
-      let getMean = (t: t) : float => {
-        let discreteProbabilityMassFraction = t.discreteProbabilityMassFraction;
-        let mean = switch(discreteProbabilityMassFraction){
-          | 1.0 => Discrete.T.getMean(t.discrete);
-          | 0.0 => Continuous.T.getMean(t.continuous);
-          | _ => (Discrete.T.getMean(t.discrete) *. discreteProbabilityMassFraction)
-            +. (Continuous.T.getMean(t.continuous) *. (1.0 -. discreteProbabilityMassFraction))
+      let getMean = (t: t): float => {
+        let discreteProbabilityMassFraction =
+          t.discreteProbabilityMassFraction;
+        switch (discreteProbabilityMassFraction) {
+        | 1.0 => Discrete.T.getMean(t.discrete)
+        | 0.0 => Continuous.T.getMean(t.continuous)
+        | _ =>
+          Discrete.T.getMean(t.discrete)
+          *. discreteProbabilityMassFraction
+          +. Continuous.T.getMean(t.continuous)
+          *. (1.0 -. discreteProbabilityMassFraction)
         };
-        mean;
       };
 
-      let getVariance = (t: t) : float => {
-        let discreteProbabilityMassFraction = t.discreteProbabilityMassFraction;
+      let getVariance = (t: t): float => {
+        let discreteProbabilityMassFraction =
+          t.discreteProbabilityMassFraction;
         let getMeanOfSquares = (t: t) => {
-          Discrete.T.getMean(XYShape.Analysis.squareXYShape(t.discrete))*.t.discreteProbabilityMassFraction
-          +. XYShape.Analysis.getMeanOfSquaresContinuousShape(t.continuous)*.(1.0 -. t.discreteProbabilityMassFraction)
+          Discrete.T.getMean(XYShape.Analysis.squareXYShape(t.discrete))
+          *. t.discreteProbabilityMassFraction
+          +. XYShape.Analysis.getMeanOfSquaresContinuousShape(t.continuous)
+          *. (1.0 -. t.discreteProbabilityMassFraction);
         };
-        switch(discreteProbabilityMassFraction){
-          | 1.0 => Discrete.T.getVariance(t.discrete);
-          | 0.0 => Continuous.T.getVariance(t.continuous);
-          | _ => XYShape.Analysis.getVarianceDangerously(t, getMean, getMeanOfSquares);
+        switch (discreteProbabilityMassFraction) {
+        | 1.0 => Discrete.T.getVariance(t.discrete)
+        | 0.0 => Continuous.T.getVariance(t.continuous)
+        | _ =>
+          XYShape.Analysis.getVarianceDangerously(
+            t,
+            getMean,
+            getMeanOfSquares,
+          )
         };
       };
     });
@@ -520,18 +546,20 @@ module Shape = {
           Discrete.T.mapY(fn),
           Continuous.T.mapY(fn),
         ));
-      
-      let getMean = (t: t): float => switch (t) {
-        | Mixed(m) => Mixed.T.getMean(m);
-        | Discrete(m) => Discrete.T.getMean(m);
-        | Continuous(m) => Continuous.T.getMean(m);
-      };
 
-      let getVariance = (t: t): float => switch (t) {
-        | Mixed(m) => Mixed.T.getVariance(m);
-        | Discrete(m) => Discrete.T.getVariance(m);
-        | Continuous(m) => Continuous.T.getVariance(m);
-      };
+      let getMean = (t: t): float =>
+        switch (t) {
+        | Mixed(m) => Mixed.T.getMean(m)
+        | Discrete(m) => Discrete.T.getMean(m)
+        | Continuous(m) => Continuous.T.getMean(m)
+        };
+
+      let getVariance = (t: t): float =>
+        switch (t) {
+        | Mixed(m) => Mixed.T.getVariance(m)
+        | Discrete(m) => Discrete.T.getVariance(m)
+        | Continuous(m) => Continuous.T.getVariance(m)
+        };
     });
 };
 
diff --git a/src/distPlus/distribution/XYShape.re b/src/distPlus/distribution/XYShape.re
index 03bddc92..9ec5a2b2 100644
--- a/src/distPlus/distribution/XYShape.re
+++ b/src/distPlus/distribution/XYShape.re
@@ -17,7 +17,7 @@ module T = {
   type ts = array(xyShape);
   let xs = (t: t) => t.xs;
   let ys = (t: t) => t.ys;
-  let empty = ({xs: [||], ys: [||]});
+  let empty = {xs: [||], ys: [||]};
   let minX = (t: t) => t |> xs |> E.A.Sorted.min |> extImp;
   let maxX = (t: t) => t |> xs |> E.A.Sorted.max |> extImp;
   let firstY = (t: t) => t |> ys |> E.A.first |> extImp;
@@ -299,55 +299,62 @@ let logScorePoint = (sampleCount, t1, t2) =>
   |> E.O.fmap(Pairs.last)
   |> E.O.fmap(Pairs.y);
 
-
-module Analysis = { 
-  let integrateContinuousShape = (
-    ~indefiniteIntegralStepwise = (p,h1) => (h1*.(p**2.0)/. 2.0), 
-    ~indefiniteIntegralLinear = (p, a, b) => (a *. (p ** 2.0) /.2.0) +. (b *. (p**3.0) /. 3.0),
-    t: DistTypes.continuousShape
-    ): float => {
+module Analysis = {
+  let integrateContinuousShape =
+      (
+        ~indefiniteIntegralStepwise=(p, h1) => h1 *. p,
+        ~indefiniteIntegralLinear=(p, a, b) => a *. p +. b *. p ** 2.0 /. 2.0,
+        t: DistTypes.continuousShape,
+      )
+      : float => {
     let xs = t.xyShape.xs;
     let ys = t.xyShape.ys;
 
-    E.A.reducei(xs, 0.0, (acc, _x, i) => {          
-      let areaUnderIntegral = switch(t.interpolation, i){
-        | (_, 0) => 0.0;
-        | (`Stepwise, _) => indefiniteIntegralStepwise(xs[i],ys[i-1]) 
-          -. indefiniteIntegralStepwise(xs[i-1],ys[i-1]);
-        | (`Linear, _) => {
-          let x1 = xs[i-1];
-          let x2 = xs[i];
-          let h1 = ys[i-1];
-          let h2 = ys[i];
-          let b = (h1 -. h2 ) /. (x1 -.x2)
-          let a = h1 -. b *.x1;
-          indefiniteIntegralLinear(x2, a, b) -. indefiniteIntegralLinear(x1, a, b);
+    E.A.reducei(
+      xs,
+      0.0,
+      (acc, _x, i) => {
+        let areaUnderIntegral =
+          switch (t.interpolation, i) {
+          | (_, 0) => 0.0
+          | (`Stepwise, _) =>
+            indefiniteIntegralStepwise(xs[i], ys[i - 1])
+            -. indefiniteIntegralStepwise(xs[i - 1], ys[i - 1])
+          | (`Linear, _) =>
+            let x1 = xs[i - 1];
+            let x2 = xs[i];
+            let h1 = ys[i - 1];
+            let h2 = ys[i];
+            let b = (h1 -. h2) /. (x1 -. x2);
+            let a = h1 -. b *. x1;
+            indefiniteIntegralLinear(x2, a, b)
+            -. indefiniteIntegralLinear(x1, a, b);
           };
-      };
-      acc +. areaUnderIntegral;
-    });
+        acc +. areaUnderIntegral;
+      },
+    );
   };
-  let getVarianceDangerously =  (
-    t: 't, 
-    getMean: ('t => float), 
-    getMeanOfSquares: ('t => float), 
-  ): float => {
-
-    let meanSquared = getMean(t)**2.0; 
-    let meanOfSquares = getMeanOfSquares(t);
-
-    meanOfSquares -. meanSquared;
-  };
-  
-  let squareXYShape = t: DistTypes.xyShape => {...t, xs: E.A.fmap(x => x**2.0, t.xs)};
 
   let getMeanOfSquaresContinuousShape = (t: DistTypes.continuousShape) => {
-    let indefiniteIntegralLinear = (p, a, b) => (a *. (p ** 3.0) /.3.0) +. (b *. (p**4.0) /. 4.0);
-    let indefiniteIntegralStepwise = (p,h1) => h1*.(p**3.0)/. 3.0;  
+    let indefiniteIntegralLinear = (p, a, b) =>
+      a *. p ** 3.0 /. 3.0 +. b *. p ** 4.0 /. 4.0;
+    let indefiniteIntegralStepwise = (p, h1) => h1 *. p ** 3.0 /. 3.0;
     integrateContinuousShape(
       ~indefiniteIntegralStepwise,
       ~indefiniteIntegralLinear,
-      t
+      t,
     );
-  }
+  };
+
+  let getVarianceDangerously =
+      (t: 't, getMean: 't => float, getMeanOfSquares: 't => float): float => {
+    let meanSquared = getMean(t) ** 2.0;
+    let meanOfSquares = getMeanOfSquares(t);
+    meanOfSquares -. meanSquared;
+  };
+
+  let squareXYShape = (t): DistTypes.xyShape => {
+    ...t,
+    xs: E.A.fmap(x => x ** 2.0, t.xs),
+  };
 };
\ No newline at end of file