diff --git a/src/go/build/deps_test.go b/src/go/build/deps_test.go
index 5b971b93e2..1ddf8f69be 100644
--- a/src/go/build/deps_test.go
+++ b/src/go/build/deps_test.go
@@ -543,7 +543,10 @@ var depsRules = `
 	internal/fuzz, internal/testlog, runtime/pprof, regexp
 	< testing/internal/testdeps;
 
-	OS, flag, testing, internal/cfg
+	MATH, errors, testing
+	< internal/testmath;
+
+	OS, flag, testing, internal/cfg, internal/testmath
 	< internal/testenv;
 
 	OS, encoding/base64
diff --git a/src/internal/testmath/ttest.go b/src/internal/testmath/ttest.go
new file mode 100644
index 0000000000..d15d2deebb
--- /dev/null
+++ b/src/internal/testmath/ttest.go
@@ -0,0 +1,213 @@
+// Copyright 2022 The Go Authors. All rights reserved.
+// Use of this source code is governed by a BSD-style
+// license that can be found in the LICENSE file.
+
+package testmath
+
+import (
+	"errors"
+	"math"
+)
+
+// A TTestSample is a sample that can be used for a one or two sample
+// t-test.
+type TTestSample interface {
+	Weight() float64
+	Mean() float64
+	Variance() float64
+}
+
+var (
+	ErrSampleSize        = errors.New("sample is too small")
+	ErrZeroVariance      = errors.New("sample has zero variance")
+	ErrMismatchedSamples = errors.New("samples have different lengths")
+)
+
+// TwoSampleWelchTTest performs a two-sample (unpaired) Welch's t-test
+// on samples x1 and x2. This t-test does not assume the distributions
+// have equal variance.
+func TwoSampleWelchTTest(x1, x2 TTestSample, alt LocationHypothesis) (*TTestResult, error) {
+	n1, n2 := x1.Weight(), x2.Weight()
+	if n1 <= 1 || n2 <= 1 {
+		// TODO: Can we still do this with n == 1?
+		return nil, ErrSampleSize
+	}
+	v1, v2 := x1.Variance(), x2.Variance()
+	if v1 == 0 && v2 == 0 {
+		return nil, ErrZeroVariance
+	}
+
+	dof := math.Pow(v1/n1+v2/n2, 2) /
+		(math.Pow(v1/n1, 2)/(n1-1) + math.Pow(v2/n2, 2)/(n2-1))
+	s := math.Sqrt(v1/n1 + v2/n2)
+	t := (x1.Mean() - x2.Mean()) / s
+	return newTTestResult(int(n1), int(n2), t, dof, alt), nil
+}
+
+// A TTestResult is the result of a t-test.
+type TTestResult struct {
+	// N1 and N2 are the sizes of the input samples. For a
+	// one-sample t-test, N2 is 0.
+	N1, N2 int
+
+	// T is the value of the t-statistic for this t-test.
+	T float64
+
+	// DoF is the degrees of freedom for this t-test.
+	DoF float64
+
+	// AltHypothesis specifies the alternative hypothesis tested
+	// by this test against the null hypothesis that there is no
+	// difference in the means of the samples.
+	AltHypothesis LocationHypothesis
+
+	// P is p-value for this t-test for the given null hypothesis.
+	P float64
+}
+
+func newTTestResult(n1, n2 int, t, dof float64, alt LocationHypothesis) *TTestResult {
+	dist := TDist{dof}
+	var p float64
+	switch alt {
+	case LocationDiffers:
+		p = 2 * (1 - dist.CDF(math.Abs(t)))
+	case LocationLess:
+		p = dist.CDF(t)
+	case LocationGreater:
+		p = 1 - dist.CDF(t)
+	}
+	return &TTestResult{N1: n1, N2: n2, T: t, DoF: dof, AltHypothesis: alt, P: p}
+}
+
+// A LocationHypothesis specifies the alternative hypothesis of a
+// location test such as a t-test or a Mann-Whitney U-test. The
+// default (zero) value is to test against the alternative hypothesis
+// that they differ.
+type LocationHypothesis int
+
+const (
+	// LocationLess specifies the alternative hypothesis that the
+	// location of the first sample is less than the second. This
+	// is a one-tailed test.
+	LocationLess LocationHypothesis = -1
+
+	// LocationDiffers specifies the alternative hypothesis that
+	// the locations of the two samples are not equal. This is a
+	// two-tailed test.
+	LocationDiffers LocationHypothesis = 0
+
+	// LocationGreater specifies the alternative hypothesis that
+	// the location of the first sample is greater than the
+	// second. This is a one-tailed test.
+	LocationGreater LocationHypothesis = 1
+)
+
+// A TDist is a Student's t-distribution with V degrees of freedom.
+type TDist struct {
+	V float64
+}
+
+// PDF returns the value at x of the probability distribution function for the
+// distribution.
+func (t TDist) PDF(x float64) float64 {
+	return math.Exp(lgamma((t.V+1)/2)-lgamma(t.V/2)) /
+		math.Sqrt(t.V*math.Pi) * math.Pow(1+(x*x)/t.V, -(t.V+1)/2)
+}
+
+// CDF returns the value at x of the cumulative distribution function for the
+// distribution.
+func (t TDist) CDF(x float64) float64 {
+	if x == 0 {
+		return 0.5
+	} else if x > 0 {
+		return 1 - 0.5*betaInc(t.V/(t.V+x*x), t.V/2, 0.5)
+	} else if x < 0 {
+		return 1 - t.CDF(-x)
+	} else {
+		return math.NaN()
+	}
+}
+
+func (t TDist) Bounds() (float64, float64) {
+	return -4, 4
+}
+
+func lgamma(x float64) float64 {
+	y, _ := math.Lgamma(x)
+	return y
+}
+
+// betaInc returns the value of the regularized incomplete beta
+// function Iₓ(a, b) = 1 / B(a, b) * ∫₀ˣ tᵃ⁻¹ (1-t)ᵇ⁻¹ dt.
+//
+// This is not to be confused with the "incomplete beta function",
+// which can be computed as BetaInc(x, a, b)*Beta(a, b).
+//
+// If x < 0 or x > 1, returns NaN.
+func betaInc(x, a, b float64) float64 {
+	// Based on Numerical Recipes in C, section 6.4. This uses the
+	// continued fraction definition of I:
+	//
+	//  (xᵃ*(1-x)ᵇ)/(a*B(a,b)) * (1/(1+(d₁/(1+(d₂/(1+...))))))
+	//
+	// where B(a,b) is the beta function and
+	//
+	//  d_{2m+1} = -(a+m)(a+b+m)x/((a+2m)(a+2m+1))
+	//  d_{2m}   = m(b-m)x/((a+2m-1)(a+2m))
+	if x < 0 || x > 1 {
+		return math.NaN()
+	}
+	bt := 0.0
+	if 0 < x && x < 1 {
+		// Compute the coefficient before the continued
+		// fraction.
+		bt = math.Exp(lgamma(a+b) - lgamma(a) - lgamma(b) +
+			a*math.Log(x) + b*math.Log(1-x))
+	}
+	if x < (a+1)/(a+b+2) {
+		// Compute continued fraction directly.
+		return bt * betacf(x, a, b) / a
+	} else {
+		// Compute continued fraction after symmetry transform.
+		return 1 - bt*betacf(1-x, b, a)/b
+	}
+}
+
+// betacf is the continued fraction component of the regularized
+// incomplete beta function Iₓ(a, b).
+func betacf(x, a, b float64) float64 {
+	const maxIterations = 200
+	const epsilon = 3e-14
+
+	raiseZero := func(z float64) float64 {
+		if math.Abs(z) < math.SmallestNonzeroFloat64 {
+			return math.SmallestNonzeroFloat64
+		}
+		return z
+	}
+
+	c := 1.0
+	d := 1 / raiseZero(1-(a+b)*x/(a+1))
+	h := d
+	for m := 1; m <= maxIterations; m++ {
+		mf := float64(m)
+
+		// Even step of the recurrence.
+		numer := mf * (b - mf) * x / ((a + 2*mf - 1) * (a + 2*mf))
+		d = 1 / raiseZero(1+numer*d)
+		c = raiseZero(1 + numer/c)
+		h *= d * c
+
+		// Odd step of the recurrence.
+		numer = -(a + mf) * (a + b + mf) * x / ((a + 2*mf) * (a + 2*mf + 1))
+		d = 1 / raiseZero(1+numer*d)
+		c = raiseZero(1 + numer/c)
+		hfac := d * c
+		h *= hfac
+
+		if math.Abs(hfac-1) < epsilon {
+			return h
+		}
+	}
+	panic("betainc: a or b too big; failed to converge")
+}