When comparing means across more than two groups, doing separate t-tests for every pair dramatically increases the chance of a false positive due to multiple comparisons. The standard approach is to use analysis of variance (ANOVA), which assesses whether any group mean differs from the others while keeping the overall Type I error rate at the chosen level. If ANOVA shows a significant effect, follow with post hoc tests (such as Tukey or Bonferroni) to determine which specific groups differ, with adjustments to control for multiple testing. Chi-square and Fisher exact tests are for categorical outcomes, not means, so they aren’t appropriate here.

Interaction between two factors is what a factorial design with two factors looks to detect. Two-way ANOVA is built to test not only the independent effects of each factor but also whether one factor’s effect depends on the level of the other—i.e., the interaction. In a scenario with age group and treatment, two-way ANOVA asks: does the treatment effect vary across age groups? If you find a significant interaction, it means the treatment works differently depending on age, so you’d examine simple effects (the treatment effect within each age group) to understand exactly where differences lie. The main effects tell you overall differences by age and by treatment, but only the interaction reveals dependence of one factor’s effect on the other. One-way ANOVA only handles a single factor, so it can’t assess how two factors influence each other. Repeated measures ANOVA handles within-subjects or repeated measurements, not a separate between-subjects interaction of two factors, unless the design explicitly treats one factor as within-subject. Thus, for analyzing the interaction between age group and treatment, two-way ANOVA is the appropriate choice.

This question is about payment reform in primary care and whether funding a wide, coordinated primary care approach can improve health outcomes while lowering overall costs. Comprehensive Primary Care Plus is designed to test exactly that by providing practices with enhanced, reimbursed primary care services plus support for care coordination, access, and population health management. The idea is that paying for comprehensive, team-based primary care with shared savings can lead to better patient outcomes and reduce total cost of care. Other initiatives focus on different aims. The Care Transitions Program targets reducing readmissions after hospital discharge, not broad primary care reimbursement and cost outcomes. Independence at Home concentrates on delivering primary care in the patient’s home with its own payment model, but it’s more about a specific delivery setting than a broad, reimbursable primary care expansion. The Open Payments Program is about transparency of payments from manufacturers to providers and does not test care delivery or cost outcomes. So the one that examines whether reimbursable comprehensive primary care can lead to better outcomes at reduced costs is Comprehensive Primary Care Plus.

HCPCS Level II codes are used for items and services not described by CPT, covering non-physician items, supplies, equipment, and certain transportation or drugs that aren’t captured in CPT descriptions. The most representative example is ambulances and durable medical equipment (DME): ambulance transport has its own HCPCS categories, and DME includes items like wheelchairs, walkers, and home medical devices all coded outside CPT. Dental services are typically coded with CDT, not HCPCS, and inpatient hospital services are usually documented with ICD-10-CM/ICD-10-PCS (and related facility billing), not Level II codes. Some drugs can be coded with HCPCS Level II (J codes), but when asked about the types of products or services specifically covered by Level II, ambulances and DME best illustrate the typical scope.

The main idea is that ARRA through the HITECH Act gave CMS the authority to create EHR incentive programs aimed at encouraging providers to use electronic health records in meaningful ways and to enable interoperability across systems. This means incentivizing adoption as well as the quality and shareability of information, so EHRs can exchange data to improve care. That makes the correct option the one that states it authorized EHR incentive programs to promote meaningful use and interoperability. The other statements contradict the Act’s purpose: the legislation was designed to increase, not decrease, EHR adoption; CMS was not limited to clinical trials; and interoperability was a central goal, not something the program ignored.