Let ${\delta }_t=\sqrt{p_t}-\sqrt{p}=(t/2)g\sqrt{p}+r_t$, with $\Vert r_t{\Vert }_2=o(|t|)$. Since $p_t-p=2\sqrt{p}{\delta }_t+{\delta }_t^2$, the Cauchy-Schwarz inequality gives $\Vert p_t-p-tpg{\Vert }_1\le 2\Vert r_t{\Vert }_2+\Vert {\delta }_t{\Vert }_2^2=o(|t|)$. Thus differentiability in quadratic mean suffices to differentiate bounded density integrals, even when the density itself has no useful pointwise derivative.

An experimental unit is a unit to which a treatment is assigned under the experimental allocation. A whole orchard can be an experimental unit even when responses are recorded separately on its trees. Different factors in a split-plot design can have different experimental units.

Replication in experimental design repeats a treatment on multiple independent experimental units, so that variation between those units can inform a treatment contrast and its standard error. Repeated measurements on one experimental unit can improve measurement precision without providing additional independent treatment replication. The experimental unit is identified by where the treatment is applied, rather than by how many responses are recorded.^[ref]

For a constructed example, grow one plant in each of eight separate pots. Apply treatment A to four pots and treatment B to four pots, without sharing drainage water between pots. Measure the length of three leaves per pot. The pot is the experimental unit; the leaf is the observational unit. There are four treatment replicates per group, eight experimental units overall, and $8\cdot 3=24$ measurements. The three leaves in one pot are nested measurements, because they share the same plant and treatment application. The setup assumes that pots do not affect one another; simply putting them in separate containers does not establish statistical independence.

Randomization chooses which experimental units receive which treatments; replication supplies multiple experimental units receiving the same treatment. These solve different problems. In the balanced allocation above, choose the four A pots uniformly among the $\left(\genfrac{}{}{0ex}{}{8}{4}\right)=70$ possible subsets. A particular pot has probability $1/2$ of receiving A. Given that one pot receives A, a second receives A with conditional probability $3/7$, rather than $1/2$. Thus the assignments are not independent random variables, even though the experimental units can supply independent responses conditional on those assignments in a suitable statistical model.

An actual ecology experiment illustrates why the distinction matters. In Stuart H. Hurlbert's account, twelve experimental ponds initially received zero, fifty or 450 fish, with five, four and three ponds respectively. Preparation included assessing variation between ponds before introducing fish; allocation considered both location and existing differences between ponds. The zero-fish ponds supplied the scientific control. These were repeated treatment applications to ponds, not twelve samples from one pond.^[ref]

The original article followed the southern California ponds for ten months. Its readouts included zooplankton populations and total phytoplankton; it reported decreases in some zooplankton populations and increased total phytoplankton.^[ref] Repeated sampling within a pond helps describe its response through time, while ponds supply treatment replication. Unequal group sizes and initial pond variation matter for the statistical model; the treatment counts alone do not establish equal variances or independent responses.

A block groups experimental units expected to have similar background responses. Comparing treatments within blocks can reduce the variance of an estimator. Block allocation must be specified before responses are used to evaluate treatment effects.

A completely randomized design assigns treatments directly to available experimental units using complete randomization, without blocking. Fixed group sizes can be enforced. The residual then includes uncontrolled between-unit variation.

A crossover design assigns each subject a sequence of treatments in successive periods. Subject blocking can reduce between-subject variation; balanced sequences can separate treatment from period effects. A carryover effect or irreversible change can invalidate a simple within-subject comparison.

A factorial design includes every combination of specified factor levels. Balanced replication permits separate estimation of marginal effects and interaction terms. The factor allocation, not the number of subsamples, determines the appropriate error term for each effect.

A split-plot design randomizes one factor to whole plots and another to subplots within each whole plot. Whole-plot treatment effects and subplot treatment effects therefore use different error ANOVA strata. Whole plots provide replication for the first factor; their subplots do not supply extra independent whole-plot replication. Within-whole-plot comparisons can remove a shared additive whole-plot variance component.

In a bipartite graph with parts of sizes $m,n$ and density $\delta >0$, a neighbourhood $V^{\prime }$ of size at least $\delta m/2$ can be chosen so that at least a proportion $1-2ϵ/{\delta }^2$ of its ordered pairs have at least $ϵn$ common neighbours. Choose a random vertex on the other side, use the second moment of its degree, and penalize pairs with few common neighbours. This is a simple dependent random choice tool for controlling restricted sumsets.

The ocean mixed layer is a near-surface layer with relatively weak vertical variations of temperature and mass density, maintained by mixing. Beneath it, stable density stratification can isolate colder water from recent surface heating. The mixed-layer depth is not necessarily the full local water depth.

A perturbation can have negligible Hellinger distance but a large integral of its density's fourth power. At the uniform density on $[0,1]$, let $b(s)=6s(1-s)$ on $[0,1]$ and zero elsewhere, and let $f_t(u)=1-t^4+t^{-2}b(u/t^6)$. Then $\int f_t=1$ and $\int (\sqrt{f_t}-1{)}^2\le 2t^4=o(t^2)$, so the score function is zero. However $\int f_t^4\ge 72/(35t^2)$. Thus the density fourth-power functional is not continuous along this differentiable-in-quadratic-mean path, despite a bounded baseline and individually bounded nearby densities.

The squared L2 norm of a probability density function derivative measures roughness and appears in optimal smoothing constants. For $r=2$, its directional derivative along a smooth compactly supported perturbation $a$ is $2\int f^{\prime \prime }{a}^{\prime \prime }$. When integration by parts is valid twice, this equals $2\int f^{(4)}a$. Thus regular root-$n$ estimation requires substantially more regularity than merely estimating a smooth probability density function.

For the convention that larger conditions are stronger, density above $p$ means every strengthening of $p$ has a further strengthening in $D$. It is the extension-cone density usually called dense below a forcing condition when smaller conditions are stronger. The sign must be translated with the order convention, rather than copied unchanged.

If $r_n(X_n-s)$ converges in distribution to $X$ and $\Phi$ is Hadamard differentiable at $s$ tangentially to a space containing the limit, the displayed transformation rule holds under the usual measurability and tight separable-support conditions. The variables $X_n$ must belong to the domain of $\Phi$.

Define mapping degree for $S^0$ using its rank-one reduced homology group ${\tilde{H}}_0(S^0;\mathbb{Z})$. Its generator is the difference of its two points. The four maps of that two-point set consist of the identity, the interchange, and two constant maps; their degree multipliers are $1,-1,0,0$. Thus sphere maps of arbitrary integer degree require positive sphere dimension.

For an impulsive distant source of deep-water gravity waves, a component of period $T$ travels at group velocity $gT/(4\pi )$. Its arrival at distance $L$ obeys the displayed law, so inverse period increases linearly with arrival time. Two arrivals determine both $L$ and $t_0$. Finite source duration, currents, refraction and passage through sea ice can invalidate the simple inversion.

A Boolean function is evasive when every deterministic decision tree computing it has a worst-case path that queries all input bits. The alternating-sum criterion for decision-tree evasiveness is one sufficient method for proving this lower bound.

A query certificate fixes some input coordinates to their values in a chosen input and must force the output for every completion. The smallest such size is $C(f,x)$; maximizing over accepted or rejected inputs gives $C_1(f)$ or $C_0(f)$, and maximizing both gives $C(f)$. A one-sided maximum over an empty output class is defined as zero. This is query certificate complexity, distinct from polynomial verification of a witness in NP.

A covariant set-valued functor $F$ is a decidable object precisely when every transition map $F(u)$ is injective. The pointwise complement of its diagonal consists of unequal pairs; it is a subfunctor exactly when transition maps preserve inequality. For left M-sets, this says that every action map is injective.

Pore velocity is the fluid velocity averaged over pore volume relative to the solid framework. For uniform porosity $\varphi$, full saturation and all pores participating, $\mathbf{v}=\mathbf{q}/\varphi$, with Darcy velocity $\mathbf{q}$ defined per bulk area. The identity follows from volume averaging: extending the relative fluid velocity by zero in the solid gives a bulk average equal to the pore fraction times the pore-volume average. It does not assert that every local passage has open area $\varphi A$ or identical speed.

In a uniform advective model, travel over length $L$ takes $L/v=\varphi L/q$. If some pore volume is inaccessible to transport, the corresponding estimate uses effective porosity ${\varphi }_e$ and $v=q/{\varphi }_e$. A connected but nearly stagnant passage can exchange solute slowly, so even this mean does not describe all arrival times. Flux-weighted residence time in a porous layer explains one precise averaging convention.^[ref]