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Use individual stratified randomization within each prison, with concealed centrally generated permuted-block randomization and varying block sizes. Prison can affect both baseline risk and intervention delivery, so balancing assignments within prison protects against imbalance in that important covariate. Varying block sizes and allocation concealment prevent staff from predicting the next allocation and selecting participants accordingly. Prespecify how incomplete final blocks are treated. To enforce the overall 4000:4000 allocation exactly, coordinate the stratum totals centrally, allocating odd final places through a concealed random rule; exact half allocation within every prison is impossible if some prison totals are odd.

Individual randomization stratified by prison is preferable to randomizing only twenty whole prisons for this design. A cluster-randomized trial would have few independent units and could lose substantial precision through intraclass correlation. Sharing or selling the intervention can cause treatment contamination, so record it and retain an intention-to-treat analysis; substantial spillover might justify a separately powered cluster design rather than being ignored.

A court-based randomized controlled trial would assign eligible offenders between the two sentencing policies using randomization, ideally stratified by court and important prespecified risk factors. With allocation concealment, the next assignment cannot be selected to suit an offender's prognosis. Baseline characteristics, both recorded and unrecorded, are balanced in distribution rather than confounded with judges' sentencing preferences. This makes a comparison of outcomes interpretable as a causal effect of assignment, subject to complete and comparable follow-up, whereas separate historical cohorts can differ in severity, social circumstances or calendar period.

The commission should compare both specified outcomes over the same 360-day period from sentencing, using an intention-to-treat analysis. An offender initially assigned community service remains in that group even if the order is later revoked and prison follows. Revocation is part of the policy's consequences; restricting the comparison to completers would select on a post-assignment outcome and lose the protection of randomization.

Within-court individual randomization also provides an objective allocation procedure and valid uncertainty estimates. It does not guarantee identical characteristics in a particular finite sample or eliminate loss to follow-up, unequal outcome ascertainment or limited generalizability. Eligibility, genuine uncertainty about the two policies, and a feasible authorized allocation mechanism would need agreement before the trial. If whole courts rather than individuals were assigned, that would be a cluster-randomized trial and its correlation structure would require a different sample size calculation.

This is a family-level cluster-randomized trial, but there are only about 1.1 contributing children per family. Most families therefore contribute a single eligible child, leaving little opportunity for within-family intraclass correlation to add much uncertainty. The equal-size exchangeable approximation has design effect $1+(m-1)\rho$, with cluster size $m$ and correlation $\rho$; when $m$ is close to one, this is close to one. A multilevel Poisson regression can consequently give nearly the same standard error as the independent-child working analysis.

Agreement is compatible with limited within-family dependence and little change from covariate adjustment; it does not prove all dependence absent. Mean family size alone does not determine the exact design effect for highly unequal cluster sizes.

Larger differences would be expected with larger families and stronger within-family correlation, especially with unequal family sizes. Pronounced between-family heterogeneity, recurrent attendances concentrated in particular children, or important imbalanced covariates can also create overdispersion or change the adjusted comparison.

An ethical objection concerns the proposed saving of control-prison staff costs: the trial is explicitly based on individual informed consent, and institutional allocation does not justify omitting control participants' information, consent or appropriate outcome follow-up. These responsibilities remain in both arms. A cluster-randomized trial can sometimes be ethically defensible, but changing the randomized unit alone does not make those participant protections unnecessary.

The statistical objection is loss of independent information and potential prison-level imbalance. Outcomes within a prison may share baseline risks, management practices and release conditions, yielding positive intraclass correlation. For equal cluster size $m$, the usual exchangeable approximation gives design effect $1+(m-1)\rho$, so the individual-randomization calculation understates the sample required when $\rho >0$. There are also fewer independent randomized units, and prison-specific adoption of management practices can be unbalanced across arms. Thus randomizing prisons generally loses precision for this individually deliverable intervention, unless compensating design features and additional clusters are supplied.