diff --git a/LMLExplore/EValues/EffectiveSet.lean b/LMLExplore/EValues/EffectiveSet.lean
new file mode 100644
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+++ b/LMLExplore/EValues/EffectiveSet.lean
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+/-
+Copyright (c) 2026 LMLExplore contributors. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: LMLExplore contributors
+-/
+module
+
+public import Mathlib.Analysis.Convex.Hull
+public import Mathlib.Probability.Kernel.Composition.MeasureComp
+public import Statlib.EValues.RIPr
+public import LMLExplore.MeasureTheory.Integral.Lebesgue.Setwise
+
+/-!
+# The effective null
+
+The effective null of a set of measures `S` is Statlib's `effectiveSet S`: the measures `μ` under
+which every e-variable for `S` has `∫⁻ X ∂μ ≤ 1`. It is the set of measures against which `S`
+cannot be distinguished by e-variables, and the duality
+`maxUtility P S logUtility = ⨅ μ ∈ effectiveSet S, klIntegral P μ` of Statlib minimizes over it.
+This file describes its structure, for any set of measures `S` on any measurable space, with no
+dominating measure:
+
+* it contains the members of `S` of mass at most `1`, and every mixture `κ ∘ₘ π` of members of
+  the effective null over a measure `π` of mass at most `1`, hence, when the members of `S` have
+  mass at most `1`, every such mixture of members of `S` and the convex hull of `S`;
+* it is solid (`μ ≤ ν ∈ effectiveSet S` gives `μ ∈ effectiveSet S`) and closed under setwise
+  convergence, and more generally under limits `μ` with `μ s ≤ liminf (fun i ↦ μs i s) l` for
+  every measurable set `s`;
+* when the members of `S` are probability measures, `S` and the probability measures of
+  `effectiveSet S` have the same e-variables, so that the effective null of the latter is
+  `effectiveSet S` again;
+* for a null of probability measures, the effective null is the set of alternatives against which
+  no level-`α` test for `S` has power larger than `α` [RW, Theorem 6.10]: a measure `Q` lies
+  outside it exactly when some test of level `α ∈ (0, 1)` for `S` has `Q`-power larger than `α`.
+
+## Main statements
+
+* `ProbabilityTheory.comp_mem_effectiveSet`, `ProbabilityTheory.comp_mem_effectiveSet_of_mem`:
+  mixtures.
+* `ProbabilityTheory.convex_effectiveSet`, `ProbabilityTheory.convexHull_subset_effectiveSet`.
+* `ProbabilityTheory.mem_effectiveSet_of_le`: solidity.
+* `ProbabilityTheory.mem_effectiveSet_of_frequently_of_forall_measure_le_liminf`: closure under
+  setwise convergence.
+* `ProbabilityTheory.isEVar_effectiveSet_inter_iff`: the same e-variables.
+* `ProbabilityTheory.mem_effectiveSet_iff_forall_bounded`: bounded e-variables suffice to test
+  membership.
+* `ProbabilityTheory.isEVar_inv_mul_of_lintegral_le`,
+  `ProbabilityTheory.IsEVar.lintegral_const_mul_le`: tests and bounded e-variables are rescalings
+  of each other [RW, Fact 3.6].
+* `ProbabilityTheory.exists_powered_test_iff`: powered tests exist exactly outside the effective
+  null [RW, Theorem 6.10].
+
+## References
+
+* [RW] A. Ramdas and R. Wang, *Hypothesis Testing with E-values*, arXiv:2410.23614v6, §6.3.1,
+  Theorem 6.10, Fact 3.6.
+* [LRR25] M. Larsson, A. Ramdas and J. Ruf, *The numeraire e-variable and reverse information
+  projection*, Ann. Statist. 53 (2025), Definition 3.1.
+-/
+
+public section
+
+open Filter Set
+open scoped ENNReal
+
+namespace ProbabilityTheory
+
+open MeasureTheory
+
+variable {𝓧 Θ ι : Type*} {m𝓧 : MeasurableSpace 𝓧} {mΘ : MeasurableSpace Θ}
+  {S T : Set (Measure 𝓧)} {μ ν : Measure 𝓧}
+
+/-- The effective null is monotone in the null. -/
+lemma effectiveSet_mono (hST : S ⊆ T) : effectiveSet S ⊆ effectiveSet T :=
+  fun _ hμ X hX ↦ hμ X (hX.anti_set hST)
+
+/-- A member of `S` of mass at most `1` belongs to the effective null of `S`. -/
+lemma mem_effectiveSet_of_mem (hμS : μ ∈ S) (hμ : μ .univ ≤ 1) : μ ∈ effectiveSet S :=
+  fun _ hX ↦ (hX.lintegral_le_measure_univ μ hμS).trans hμ
+
+/-- A member of `S` belongs to the effective null of `S` exactly when its mass is at most `1`. -/
+lemma mem_effectiveSet_iff_of_mem (hμS : μ ∈ S) : μ ∈ effectiveSet S ↔ μ .univ ≤ 1 :=
+  ⟨measure_univ_le_one_of_mem_effectiveSet, mem_effectiveSet_of_mem hμS⟩
+
+/-- A null whose members have mass at most `1`, in particular a null of probability measures, is
+contained in its effective null. -/
+lemma subset_effectiveSet (hS : ∀ μ ∈ S, μ .univ ≤ 1) : S ⊆ effectiveSet S :=
+  fun μ hμ ↦ mem_effectiveSet_of_mem hμ (hS μ hμ)
+
+/-! ### Mixtures and convexity -/
+
+/-- The effective null is closed under mixtures: for a measure `π` of mass at most `1` and a
+kernel `κ` whose values lie `π`-almost surely in the effective null, `κ ∘ₘ π` lies in the effective
+null. -/
+lemma comp_mem_effectiveSet {π : Measure Θ} {κ : Kernel Θ 𝓧}
+    (hκ : ∀ᵐ θ ∂π, κ θ ∈ effectiveSet S) (hπ : π .univ ≤ 1) :
+    κ ∘ₘ π ∈ effectiveSet S := by
+  intro X hX
+  rw [Measure.lintegral_bind κ.aemeasurable hX.measurable.aemeasurable]
+  calc ∫⁻ θ, ∫⁻ x, X x ∂κ θ ∂π
+  _ ≤ ∫⁻ _, 1 ∂π := lintegral_mono_ae (hκ.mono fun θ hθ ↦ hθ X hX)
+  _ ≤ 1 := by simpa using hπ
+
+/-- Every mixture `κ ∘ₘ π` of members of `S` of mass at most `1` over a measure `π` of mass at
+most `1`, in particular every mixture of a probability null over a probability measure, belongs to
+the effective null of `S`. -/
+lemma comp_mem_effectiveSet_of_mem (hS : ∀ μ ∈ S, μ .univ ≤ 1) {π : Measure Θ}
+    {κ : Kernel Θ 𝓧} (hκ : ∀ᵐ θ ∂π, κ θ ∈ S) (hπ : π .univ ≤ 1) :
+    κ ∘ₘ π ∈ effectiveSet S :=
+  comp_mem_effectiveSet (hκ.mono fun _ hθ ↦ subset_effectiveSet hS hθ) hπ
+
+/-- The effective null is convex. -/
+lemma convex_effectiveSet (S : Set (Measure 𝓧)) : Convex ℝ≥0∞ (effectiveSet S) := by
+  intro μ hμ ν hν a b _ _ hab X hX
+  rw [lintegral_add_measure, lintegral_smul_measure, lintegral_smul_measure]
+  calc a * ∫⁻ x, X x ∂μ + b * ∫⁻ x, X x ∂ν
+  _ ≤ a * 1 + b * 1 := by gcongr; exacts [hμ X hX, hν X hX]
+  _ = 1 := by rw [mul_one, mul_one, hab]
+
+/-- The convex hull of a null whose members have mass at most `1` is contained in its effective
+null. -/
+lemma convexHull_subset_effectiveSet (hS : ∀ μ ∈ S, μ .univ ≤ 1) :
+    convexHull ℝ≥0∞ S ⊆ effectiveSet S :=
+  convexHull_min (subset_effectiveSet hS) (convex_effectiveSet S)
+
+/-! ### Solidity and closedness -/
+
+/-- The effective null is solid: a measure below a member of the effective null belongs to it. -/
+lemma mem_effectiveSet_of_le (hμν : μ ≤ ν) (hν : ν ∈ effectiveSet S) : μ ∈ effectiveSet S :=
+  fun X hX ↦ (lintegral_mono' hμν le_rfl).trans (hν X hX)
+
+/-- The effective null is closed under setwise convergence: if `μ s ≤ liminf (fun i ↦ μs i s) l`
+for every measurable set `s`, for instance if `μs i s` tends to `μ s` along `l` for every
+measurable set `s`, and `μs i` lies frequently along `l` in the effective null, then so does
+`μ`. -/
+lemma mem_effectiveSet_of_frequently_of_forall_measure_le_liminf {l : Filter ι}
+    {μs : ι → Measure 𝓧} (h : ∀ s, MeasurableSet s → μ s ≤ liminf (fun i ↦ μs i s) l)
+    (hμs : ∃ᶠ i in l, μs i ∈ effectiveSet S) :
+    μ ∈ effectiveSet S :=
+  fun X hX ↦ lintegral_le_of_frequently_of_forall_measure_le_liminf_measure h
+    (hμs.mono fun _ hi ↦ hi X hX)
+
+/-- The effective null is closed under setwise convergence: if `μ s ≤ liminf (fun i ↦ μs i s) l`
+along a nontrivial filter `l` for every measurable set `s`, for instance if `μs i s` tends to
+`μ s` along `l` for every measurable set `s`, and `μs i` lies eventually along `l` in the
+effective null, then so does `μ`. -/
+lemma mem_effectiveSet_of_forall_measure_le_liminf {l : Filter ι} [l.NeBot]
+    {μs : ι → Measure 𝓧} (h : ∀ s, MeasurableSet s → μ s ≤ liminf (fun i ↦ μs i s) l)
+    (hμs : ∀ᶠ i in l, μs i ∈ effectiveSet S) :
+    μ ∈ effectiveSet S :=
+  mem_effectiveSet_of_frequently_of_forall_measure_le_liminf h hμs.frequently
+
+/-! ### The e-variables of the effective null -/
+
+/-- A null of probability measures and the probability measures of its effective null have the
+same e-variables. -/
+lemma isEVar_effectiveSet_inter_iff (hS : ∀ μ ∈ S, IsProbabilityMeasure μ) {X : 𝓧 → ℝ≥0∞} :
+    IsEVar X (effectiveSet S ∩ {μ | IsProbabilityMeasure μ}) ↔ IsEVar X S := by
+  refine ⟨fun hX ↦ hX.anti_set (S := S) fun μ hμ ↦ ?_, fun hX ↦ ?_⟩
+  · have := hS μ hμ
+    exact ⟨mem_effectiveSet_of_mem hμ prob_le_one, this⟩
+  · exact IsEVar.of_lintegral_le_one (S := effectiveSet S ∩ {μ | IsProbabilityMeasure μ})
+      (fun μ hμ ↦ hμ.2) hX.measurable fun μ hμ ↦ hμ.1 X hX
+
+/-- The effective null of the probability measures of the effective null of a null of probability
+measures is the effective null itself. -/
+lemma effectiveSet_effectiveSet_inter (hS : ∀ μ ∈ S, IsProbabilityMeasure μ) :
+    effectiveSet (effectiveSet S ∩ {μ | IsProbabilityMeasure μ}) = effectiveSet S := by
+  ext μ
+  simp only [mem_effectiveSet_iff, isEVar_effectiveSet_inter_iff hS]
+
+/-- Membership in the effective null of a null of finite measures can be tested with bounded
+e-variables only. -/
+lemma mem_effectiveSet_iff_forall_bounded (hS : ∀ μ ∈ S, IsFiniteMeasure μ) :
+    μ ∈ effectiveSet S ↔
+      ∀ X : 𝓧 → ℝ≥0∞, IsEVar X S → ∀ C : ℝ≥0∞, C ≠ ∞ → (∀ x, X x ≤ C) → ∫⁻ x, X x ∂μ ≤ 1 := by
+  refine ⟨fun hμ X hX _ _ _ ↦ hμ X hX, fun h X hX ↦ ?_⟩
+  -- Truncate `X` at the integers: `X = ⨆ n, min (X x) n`, and the truncations are bounded
+  -- e-variables.
+  have hX_eq : ∀ x, X x = ⨆ n : ℕ, min (X x) n := fun x ↦ by
+    rw [← inf_iSup_eq, ENNReal.iSup_natCast, inf_top_eq]
+  have hmeas : ∀ n : ℕ, Measurable fun x ↦ min (X x) n := fun n ↦ hX.measurable.min measurable_const
+  rw [lintegral_congr hX_eq,
+    lintegral_iSup hmeas fun m n hmn x ↦ min_le_min_left _ (mod_cast hmn)]
+  exact iSup_le fun n ↦ h _ (hX.mono hS (hmeas n) fun x ↦ min_le_left _ _) n
+    (ENNReal.natCast_ne_top n) fun x ↦ min_le_right _ _
+
+/-! ### Tests and the effective null -/
+
+/-- A test of level `α` is an e-variable after division by `α` [RW, Fact 3.6]: if a measurable `φ`
+has `∫⁻ φ ∂P ≤ α` for every `P` in a null of probability measures `S`, then `α⁻¹ * φ` is an
+e-variable for `S`. -/
+lemma isEVar_inv_mul_of_lintegral_le (hS : ∀ P ∈ S, IsProbabilityMeasure P) {φ : 𝓧 → ℝ≥0∞}
+    (hφ : Measurable φ) {α : ℝ≥0∞} (hφS : ∀ P ∈ S, ∫⁻ x, φ x ∂P ≤ α) :
+    IsEVar (fun x ↦ α⁻¹ * φ x) S := by
+  refine .of_lintegral_le_one hS (hφ.const_mul _) fun P hP ↦ ?_
+  rw [lintegral_const_mul _ hφ]
+  calc α⁻¹ * ∫⁻ x, φ x ∂P
+  _ ≤ α⁻¹ * α := by gcongr; exact hφS P hP
+  _ ≤ 1 := α.inv_mul_le_one
+
+/-- A multiple `c * X` of an e-variable `X` for `S` has `P`-expectation at most `c * P univ` for
+every `P ∈ S`. In particular, for an e-variable `X ≤ D` and a null of probability measures,
+`D⁻¹ * X` is a test of level `D⁻¹` [RW, Fact 3.6]. -/
+lemma IsEVar.lintegral_const_mul_le {X : 𝓧 → ℝ≥0∞} (hX : IsEVar X S) (c : ℝ≥0∞) {P : Measure 𝓧}
+    (hP : P ∈ S) :
+    ∫⁻ x, c * X x ∂P ≤ c * P .univ := by
+  rw [lintegral_const_mul _ hX.measurable]
+  gcongr
+  exact hX.lintegral_le_measure_univ P hP
+
+/-- **Powered tests exist exactly outside the effective null** [RW, Theorem 6.10]. For a null of
+probability measures `S` and a measure `Q`, some test `φ` (a measurable function with values in
+`[0, 1]`) of level `α ∈ (0, 1)` for `S` has `Q`-power `∫⁻ φ ∂Q` larger than `α` exactly when `Q`
+does not belong to the effective null of `S`. -/
+theorem exists_powered_test_iff (hS : ∀ P ∈ S, IsProbabilityMeasure P) (Q : Measure 𝓧) :
+    (∃ α : ℝ≥0∞, 0 < α ∧ α < 1 ∧ ∃ φ : 𝓧 → ℝ≥0∞, Measurable φ ∧ (∀ x, φ x ≤ 1) ∧
+        (∀ P ∈ S, ∫⁻ x, φ x ∂P ≤ α) ∧ α < ∫⁻ x, φ x ∂Q) ↔
+      Q ∉ effectiveSet S := by
+  constructor
+  · -- `φ / α` is an e-variable for `S` with `Q`-expectation larger than `1`.
+    rintro ⟨α, hα0, hα1, φ, hφ, -, hφS, hφQ⟩ hQ
+    have hQ_le := hQ _ (isEVar_inv_mul_of_lintegral_le hS hφ hφS)
+    rw [lintegral_const_mul _ hφ,
+      ENNReal.inv_mul_le_iff hα0.ne' (hα1.trans ENNReal.one_lt_top).ne, mul_one] at hQ_le
+    exact hφQ.not_ge hQ_le
+  · -- A bounded e-variable `X ≤ C` with `Q`-expectation larger than `1`, divided by
+    -- `D = max C 2`, is a test of level `D⁻¹ < 1`.
+    intro hQ
+    rw [mem_effectiveSet_iff_forall_bounded fun P hP ↦ have := hS P hP; inferInstance] at hQ
+    push Not at hQ
+    obtain ⟨X, hX, C, hC, hXC, hXQ⟩ := hQ
+    set D := max C 2 with hD
+    have hD_top : D ≠ ∞ := max_ne_top hC ENNReal.ofNat_ne_top
+    have hD0 : D ≠ 0 := (two_ne_zero.bot_lt.trans_le (le_max_right C 2)).ne'
+    refine ⟨D⁻¹, ENNReal.inv_pos.mpr hD_top,
+      ENNReal.inv_lt_one.mpr (ENNReal.one_lt_two.trans_le (le_max_right C 2)),
+      fun x ↦ D⁻¹ * X x, hX.measurable.const_mul _, fun x ↦ ?_, fun P hP ↦ ?_, ?_⟩
+    · calc D⁻¹ * X x
+      _ ≤ D⁻¹ * D := by gcongr; exact (hXC x).trans (le_max_left C 2)
+      _ ≤ 1 := D.inv_mul_le_one
+    · have := hS P hP
+      simpa using hX.lintegral_const_mul_le D⁻¹ hP
+    · rw [lintegral_const_mul _ hX.measurable]
+      simpa using ENNReal.mul_lt_mul_right (ENNReal.inv_ne_zero.mpr hD_top)
+        (ENNReal.inv_ne_top.mpr hD0) hXQ
+
+end ProbabilityTheory
diff --git a/LMLExplore/MeasureTheory/Integral/Lebesgue/Setwise.lean b/LMLExplore/MeasureTheory/Integral/Lebesgue/Setwise.lean
new file mode 100644
index 00000000..024545af
--- /dev/null
+++ b/LMLExplore/MeasureTheory/Integral/Lebesgue/Setwise.lean
@@ -0,0 +1,82 @@
+/-
+Copyright (c) 2026 LMLExplore contributors. All rights reserved.
+Released under Apache 2.0 license as described in the file LICENSE.
+Authors: LMLExplore contributors
+-/
+module
+
+public import Mathlib.MeasureTheory.Integral.Lebesgue.Basic
+public import Mathlib.Order.Filter.ENNReal
+public import TauCeti.Topology.Instances.ENNReal
+
+/-!
+# Fatou's lemma for setwise lower bounds on measures
+
+Let `μs i` be a family of measures and `μ` a measure such that `μ s ≤ liminf (fun i ↦ μs i s) l`
+for every measurable set `s`; this holds in particular when `μs i` converges setwise to `μ` along
+`l`, that is, when `μs i s` tends to `μ s` for every measurable set `s`. Then the lower Lebesgue
+integral is lower semicontinuous: `∫⁻ f ∂μ ≤ liminf (fun i ↦ ∫⁻ f ∂μs i) l` for every function
+`f : α → ℝ≥0∞`, with no measurability, finiteness or integrability assumption. This is Fatou's
+lemma with the measures, rather than the functions, varying, and the measurable-set analogue of
+the Portmanteau bound
+`MeasureTheory.lintegral_le_liminf_lintegral_of_forall_isOpen_measure_le_liminf_measure`. It
+shows, for instance, that a set of measures defined by upper bounds on the integrals of a family
+of functions is closed under setwise convergence.
+
+## Main statements
+
+* `MeasureTheory.lintegral_le_liminf_lintegral_of_forall_measure_le_liminf_measure`: the lower
+  Lebesgue integral is lower semicontinuous.
+* `MeasureTheory.lintegral_le_of_frequently_of_forall_measure_le_liminf_measure`: if
+  `∫⁻ f ∂μs i ≤ c` frequently along `l`, then `∫⁻ f ∂μ ≤ c`.
+-/
+
+public section
+
+open Filter
+open scoped ENNReal NNReal
+
+namespace MeasureTheory
+
+variable {α ι : Type*} {mα : MeasurableSpace α} {l : Filter ι} {μs : ι → Measure α}
+  {μ : Measure α}
+
+/-- **Fatou's lemma for varying measures**: if `μ s ≤ liminf (fun i ↦ μs i s) l` for every
+measurable set `s`, for instance if `μs i` converges setwise to `μ` along `l`, then the lower
+Lebesgue integral of any function under `μ` is at most the inferior limit of its integrals under
+`μs i`. -/
+theorem lintegral_le_liminf_lintegral_of_forall_measure_le_liminf_measure
+    (h : ∀ s, MeasurableSet s → μ s ≤ liminf (fun i ↦ μs i s) l) (f : α → ℝ≥0∞) :
+    ∫⁻ x, f x ∂μ ≤ liminf (fun i ↦ ∫⁻ x, f x ∂μs i) l := by
+  rcases l.eq_or_neBot with rfl | hl
+  · simp
+  rw [lintegral_eq_nnreal]
+  refine iSup₂_le fun φ hφ ↦ ?_
+  -- The integral of a simple function with finite values is a finite combination of the
+  -- measures of measurable sets, and the inferior limit is superadditive.
+  have hφ_le : (φ.map ((↑) : ℝ≥0 → ℝ≥0∞)).lintegral μ ≤
+      liminf (fun i ↦ (φ.map ((↑) : ℝ≥0 → ℝ≥0∞)).lintegral (μs i)) l := by
+    simp only [SimpleFunc.map_lintegral]
+    induction φ.range using Finset.induction_on with
+    | empty => simp
+    | insert c t hc ih =>
+      simp only [Finset.sum_insert hc]
+      refine (add_le_add ?_ ih).trans (ENNReal.le_liminf_add _ _)
+      rw [ENNReal.liminf_const_mul_of_ne_top ENNReal.coe_ne_top]
+      gcongr
+      exact h _ (φ.measurableSet_preimage _)
+  refine hφ_le.trans (liminf_le_liminf (.of_forall fun i ↦ ?_))
+  rw [← SimpleFunc.lintegral_eq_lintegral]
+  exact lintegral_mono fun x ↦ by simpa using hφ x
+
+/-- If `μ s ≤ liminf (fun i ↦ μs i s) l` for every measurable set `s`, for instance if `μs i`
+converges setwise to `μ` along `l`, and `∫⁻ f ∂μs i ≤ c` frequently along `l`, then
+`∫⁻ f ∂μ ≤ c`. -/
+theorem lintegral_le_of_frequently_of_forall_measure_le_liminf_measure
+    (h : ∀ s, MeasurableSet s → μ s ≤ liminf (fun i ↦ μs i s) l) {f : α → ℝ≥0∞}
+    {c : ℝ≥0∞} (hc : ∃ᶠ i in l, ∫⁻ x, f x ∂μs i ≤ c) :
+    ∫⁻ x, f x ∂μ ≤ c :=
+  (lintegral_le_liminf_lintegral_of_forall_measure_le_liminf_measure h f).trans
+    (liminf_le_of_frequently_le' hc)
+
+end MeasureTheory