How Mock Tests Predict Your NCLEX Readiness

Abstract

The licensure of registered and practical nurses in the United States and Canada relies on the National Council Licensure Examination (NCLEX). This high-stakes assessment serves as the final gatekeeper for entry into practice. With the implementation of the Next Generation NCLEX (NGN) in April 2023, the testing landscape underwent a fundamental shift from knowledge retrieval to the assessment of clinical judgment. This transition necessitated a parallel evolution in the predictive validity of mock assessments used by candidates to gauge readiness. This report provides an exhaustive, 15,000-word analysis of how mock testing platforms—specifically UWorld, Archer Review, Kaplan, ATI, and others—predict NCLEX readiness. By examining the psychometric underpinnings of Computerized Adaptive Testing (CAT), the statistical correlation between mock scores and pass rates, and the pedagogical mechanisms of remediation, this document establishes a definitive framework for interpreting readiness metrics in the NGN era.

1. The Psychometric Foundations of High-Stakes Licensure

To accurately interpret the predictive value of a mock test, one must first comprehend the measurement science governing the exam it simulates. The NCLEX is not a traditional criterion-referenced exam where a fixed percentage of correct answers guarantees a pass. Instead, it operates on the principles of Item Response Theory (IRT), specifically utilizing the Rasch model to estimate candidate ability.

1.1 Item Response Theory (IRT) and the Logit Scale

In the context of the NCLEX, a candidate’s competence is measured in “logits” (log-odds units), a continuous scale that represents the probability of success on items of varying difficulty. The central axiom of the Rasch model is that the probability of a candidate answering a specific item correctly is a function of the difference between the candidate’s ability and the item’s difficulty (b).

When a candidate sits for a mock exam on a sophisticated platform like UWorld or Kaplan, the underlying algorithm is attempting to replicate this probabilistic relationship. A raw score—simply the number of questions answered correctly divided by the total number of questions—is a poor proxy for ability in this framework. For instance, answering 75% of “easy” cognitive-level questions (knowledge/comprehension) yields a significantly lower ability estimate than answering 60% of “hard” cognitive-level questions (synthesis/evaluation).Valid mock assessments, therefore, do not merely report a percentage; they report a derived metric often presented as a “probability of passing.” This metric is calculated by comparing the candidate’s estimated theta against the known passing logit of the NCLEX.¹ The precision of this estimate is contingent on the size and calibration of the platform’s item bank. Platforms with large, well-calibrated banks can offer “Computer Adaptive Testing” (CAT) simulations that mimic the NCLEX’s variable length and difficulty progression, thereby providing a higher fidelity prediction than static, linear exams.

1.2 The Mechanics of Computerized Adaptive Testing (CAT)

The NCLEX utilizes CAT to maximize measurement efficiency. The test adapts to the candidate’s ability level in real-time.

The Start: Every candidate begins with an ability estimate of 0.00 logits.
The Progression: A correct answer leads to a harder subsequent item; an incorrect answer leads to an easier one.
The Stop Rule: The exam concludes when the algorithm determines with 95% confidence that the candidate’s true ability is either above or below the passing standard.

Mock tests that employ CAT simulations attempt to replicate this “uncertainty reduction” mechanism. The predictive validity of a mock CAT depends on the algorithm’s “stopping rule.” For example, if a mock test shuts off at 85 questions with a “Pass” result, the algorithm has calculated that the standard error of measurement (SEM) has shrunk sufficiently to confirm the candidate is above the passing threshold. However, discrepancies often arise because commercial platforms do not possess the exact item difficulty parameters of the NCSBN. Consequently, a mock CAT is a simulation of the experience, not a perfect replication of the psychometric measurement.

1.3 Norm-Referenced vs. Criterion-Referenced Prediction

A critical distinction in interpreting mock test scores is the difference between norm-referenced and criterion-referenced data.

Criterion-Referenced: The NCLEX is criterion-referenced. It compares the candidate against a fixed standard of safety (the passing logit). Theoretically, if every candidate meets the standard, every candidate passes.
Norm-Referenced: Most mock platforms (UWorld, Archer, Nurse Plus) rely heavily on norm-referenced data (percentiles). They compare the candidate to other users on the platform.

The predictive logic used by these platforms is inferential: “If 90% of students who score above the 40th percentile pass the NCLEX, then being in the 40th percentile predicts a pass.” This relies on the assumption that the cohort of students using the platform is representative of the general testing population. Since students purchasing premium prep courses are often more motivated and higher-performing, the “average” on these platforms is often higher than the national average. Therefore, scoring “average” (50th percentile) on a platform like UWorld often indicates a significantly higher-than-average probability of passing the actual exam.

2. The Next Generation NCLEX (NGN) Paradigm Shift

The introduction of the NGN in 2023 represented the most significant overhaul of nursing licensure in decades. This shift was driven by practice analysis studies indicating that entry-level nurses were frequently involved in errors related to clinical judgment. The new exam focuses on measuring the specific cognitive processes involved in making safe clinical decisions.

2.1 The Clinical Judgment Measurement Model (CJMM)

The NGN is built upon the NCSBN Clinical Judgment Measurement Model (CJMM), which delineates six cognitive skills that mock tests must now assess to be predictive:

Recognize Cues: Identifying relevant data from the patient’s chart or presentation.
Analyze Cues: Linking cues to a clinical condition or concern.
Prioritize Hypotheses: determining the most pressing problem.
Generate Solutions: Identifying appropriate interventions.
Take Action: Implementing the interventions.
Evaluate Outcomes: Determining if the interventions were effective.

Mock tests that rely heavily on traditional multiple-choice questions (MCQs) fail to capture the full spectrum of NGN readiness. A candidate might be proficient at “Taking Action” (memorizing pharmacology interventions) but deficient in “Recognizing Cues” (filtering relevant vs. irrelevant data). Predictive validity in the NGN era requires assessments that simulate the “unfolding” nature of case studies, where new information is presented sequentially, and decisions build upon one another.

2.2 Polytomous Scoring and the “Partial Credit” Effect

Under the previous iteration of the NCLEX, a “Select All That Apply” (SATA) question was scored dichotomously: correct or incorrect. The NGN introduced polytomous scoring (partial credit), utilizing three distinct rules:

+/- Scoring: Candidates receive +1 point for a correct selection and -1 point for an incorrect selection (with a floor of zero). This rule applies to many SATA and NGN item types.
0/1 Scoring: Candidates receive 1 point for each correct option, with no penalty for incorrect options (used in “Multiple Response: Select N” items).
Rationale Scoring: Used in dyad/triad items where understanding the cause-and-effect relationship is required for credit.

This scoring change has profoundly impacted the interpretation of mock test scores. A raw score of 50% on a mock exam, which might have been a failing grade in 2022, may now represent a passing trajectory due to the accumulation of partial credit points. However, the +/- scoring rule also introduces a penalty for “risk-taking.” Candidates who guess wildly on SATA questions can negate their correct answers, ending up with zero points for the item. Thus, mock tests now predict readiness not just by measuring knowledge, but by measuring precision—the candidate’s ability to discern exactly what is true and refrain from selecting what is uncertain.

2.3 The “Safe Nurse” Construct and Fatal Flaws

Ultimately, readiness is a measure of safety. The NGN explicitly tests for “fatal flaws”—decisions that would cause immediate harm to a patient. Mock tests predict this by tracking performance on high-stakes items. Analyzing cues incorrectly in a mock case study (e.g., administering a beta-blocker to a bradycardic patient) is weighted heavily in readiness algorithms. Readiness is not merely about the accumulation of facts; it is about demonstrating the judgment to avoid catastrophic error. Platforms that do not penalize dangerous answers in their scoring algorithms fail to accurately predict the “safety” component of the NCLEX licensure decision.

3. Comparative Market Analysis of Predictive Platforms

The landscape of NCLEX preparation is populated by several major platforms, each claiming high predictive validity. This section provides a granular analysis of the top contenders—UWorld, Archer Review, Kaplan, and ATI—evaluating their methodologies, data reliance, and user outcomes.

3.1 UWorld: The “Gold Standard” of Content Validity

UWorld is widely regarded in the nursing education community as the benchmark for content rigor and explanation depth. Its predictive mechanism relies on the strong correlation between its difficult item bank and the actual exam.

3.1.1 QBank Percentiles and NGN Calibration

Post-NGN data indicates that the median score in the UWorld QBank has shifted.

Current Medians: Approximately 70% for RN and 64% for PN.
Predictive Insight: Historically, students maintaining an average QBank score of 56% or higher demonstrated a passing rate of over 92%. With the inflation caused by NGN partial credit, a “safe” raw score is now generally considered to be consistently above 65-70%.
Rank Validity: The percentile rank is a more robust predictor than raw score. Students scoring above the 48th percentile are statistically highly likely to pass. This is because the UWorld user base is self-selected and often skews towards high-performing students; thus, being “average” on UWorld often equates to being “above average” in the general testing population.

3.1.2 Self-Assessment Exams

UWorld’s Self-Assessments are non-adaptive, 100-question exams designed to mimic the content distribution of the NCLEX test plan.

Scoring Categories: Results are categorized as “Low,” “Borderline,” “High,” and “Very High” chance of passing.
Validity Statistics: UWorld reports that students achieving a “Very High” assessment score have a 98-99% pass rate.
Thresholds:
- Very High: >72-73% Raw Score (RN).
- Borderline: 56-62% Raw Score.
Analysis: The strict inclusion criteria for their validity surveys (e.g., only including students who answered >500 questions) strengthens the credibility of these statistics. The correlation is strong because UWorld items are notoriously difficult and cognitively dense, often requiring multi-step reasoning similar to the most difficult strata of the NCLEX.

3.2 Archer Review: The “Streak” Method and Consistency

Archer Review has gained significant market share by offering a more affordable alternative to UWorld and by utilizing a distinct “vagueness” in question design that many students claim closely resembles the actual NCLEX.

3.2.1 The “Four Highs” Standard

Archer’s primary readiness metric is the “streak.” Unlike UWorld, which emphasizes the predictive value of a single self-assessment, Archer advises that a single high score is insufficient due to variance.

The Metric: Scoring “High” or “Very High” on four consecutive readiness assessments predicts a 99% pass rate.
Statistical Logic: This method mitigates the “lucky guess” factor and the standard error of measurement. A student who can replicate a “High” performance four times in a row demonstrates the stability of ability required for the actual exam.
Performance Delta: Archer claims their students show a 98.98% pass rate compared to the national average of ~87-94% for first-time US-educated nurses, representing a significant improvement delta.

3.2.2 Peer Scoring Graphics

Archer provides a unique visual interface for readiness: the Peer Score comparison.

The Graph: Students see their performance trend line against the “Peer Average” trend line.
Predictive Value: Remaining consistently above the peer line is a key indicator of readiness. This norm-referenced approach helps students contextualize their raw scores; a 55% raw score might look failing, but if the peer average for that assessment is 45%, the student is actually performing exceptionally well.

3.3 Kaplan: The Decision Tree and Strategic Focus

Kaplan’s approach focuses heavily on test-taking strategy—specifically the “Decision Tree”—rather than pure content mastery.

3.3.1 The Readiness Test

Kaplan’s Readiness Test is a high-difficulty linear exam.

The Benchmark: A raw score of 60% on the Kaplan Readiness Test is correlated with a 94% probability of passing the NCLEX.
CAT Validity: Kaplan’s internal studies show that students who receive an “Overall Green” (Pass) on their CAT practice exams have a 91% or higher probability of passing.

3.3.2 The “Red/Green” Light System

Kaplan’s CAT exams provide a simple visual result:

Green: Above passing standard.
Red: Below passing standard.
Nuance: Students often report panic when they see low raw percentages (e.g., 45-50%) even with a “Green” result. This highlights the nature of CAT: if the questions presented were at a high logit difficulty, a 45% accuracy rate can still place the student’s theta above the passing standard. Kaplan explicitly states that their CAT scores are designed to simulate the experience, while the linear Readiness Test is the better predictor.

3.4 ATI: The Institutional Gatekeeper

ATI is widely used by nursing schools as a curriculum-integrated assessment tool and exit exam.

3.4.1 The Comprehensive Predictor

ATI provides a “Predicted Probability of Passing NCLEX” based on their Comprehensive Predictor exam.

Thresholds: A score of 99% probability of passing is the gold standard, often requiring a raw score in the mid-to-high 70s or low 80s.
Discordance: There are documented cases of students scoring 99% probability on ATI and failing the NCLEX, or scoring 70% probability and passing. This discrepancy often arises because ATI tests are linear and content-heavy (“Nursing School” style), whereas the NCLEX is adaptive and judgment-heavy (“Safety” style). Students who memorize ATI books may do well on the predictor but struggle with the novelty of NGN case studies if they cannot apply that content to new scenarios.
2025 Updates: Newer versions of the ATI predictor have integrated NGN items to improve validity, with students reporting that high probabilities (98%+) are translating effectively to licensure success in the post-NGN era.

3.5 Nurse Plus Academy: The Adaptive Simulator

Nurse Plus Academy offers a distinct “NCLEX Simulator” that mimics the exam interface and adaptivity.

Pass Guarantee: They offer a money-back guarantee for premium members who pass their simulator.
User Feedback: While some users praise the realism, others note that the question bank may be smaller than UWorld’s, leading to question repetition, which can inflate scores artificially.
Algorithm: The simulator stops when a pass/fail decision is made, mimicking the variable length of the real exam.

3.6 Summary of Predictive Benchmarks

The following table synthesizes the primary predictive benchmarks for the major platforms.

Platform	Primary Readiness Tool	Key Benchmark Metric	Estimated Pass Probability	Predictive Style
UWorld	Self-Assessment Exam	“Very High” Result	98% – 99%	Content Difficulty & Application
Archer	Readiness Assessment	4 Consecutive “High/Very Highs”	99%	Consistency & Stability
Kaplan	Readiness Test	Raw Score > 60%	94%	Strategic Decision Making
ATI	Comprehensive Predictor	> 92% Probability Score	Varies	Curriculum Mastery
Nurse Plus	NCLEX Simulator	Passing Simulation	Guaranteed	Adaptive Simulation

4. Data Interpretation and Readiness Benchmarks

Understanding the data provided by these platforms requires a nuanced approach. A raw score is rarely the whole story.

4.1 The “Borderline” Candidate: A Statistical Danger Zone

Students who consistently score in the “Borderline” or “Low Pass” categories are at the highest risk of failure. Statistical variance means that on any given day, a borderline student’s theta estimate has a significant confidence interval that overlaps with the failing range.

Interpretation: For these students, mock tests accurately predict uncertainty. A “High” score on Monday followed by a “Low” score on Wednesday indicates a lack of knowledge consolidation. The standard deviation of their performance is too wide to ensure safety.
Recommendation: A “Borderline” result should be interpreted as a “Fail” for scheduling. Candidates in this zone should postpone their exam until their metrics shift to a consistent “High”.

4.2 Interpreting Percentiles vs. Raw Scores

As noted, raw scores are subject to inflation from NGN partial credit. Percentiles are generally more stable, but they depend on the reference group.

UWorld Percentiles: Being in the 50th percentile on UWorld means you are performing as well as the median UWorld student. Since UWorld students generally have a high pass rate (98%), the 50th percentile is a very safe place to be.
The “40th Percentile” Rule: Anecdotal data and forum consensus suggest that maintaining a percentile rank above 40 on reputable QBanks correlates strongly with passing, provided the student is not simply memorizing answers.

4.3 The Impact of “Unused” Questions

Predictive validity is highest when the candidate is answering new questions.

The Memorization Bias: If a student retakes a test or resets their QBank and sees questions for a second time, their score will naturally rise. This increase represents recall, not clinical judgment.
Guideline: Only scores derived from “unused” questions should be used for predictive purposes. Reused question scores are metrics of retention, not readiness.

5. The False Positive Phenomenon: Why High Scorers Fail

A persistent and distressing anomaly in NCLEX preparation is the candidate who scores “Very High” on predictors but fails the actual exam. Psychometric analysis and post-exam autopsies reveal several distinct causes for this “false positive” readiness.

5.1 The “Memorization vs. Application” Trap

A major threat to predictive validity is the misuse of QBank tools as flashcards rather than simulation engines.

The Mechanism: Students may memorize that “Patient with X symptoms gets Drug Y.”
The Failure Mode: The NCLEX presents “Patient with X symptoms” but adds a complication (e.g., “Patient also has renal failure”). The memorized answer (Drug Y) is now contraindicated. The student, relying on pattern recognition rather than physiological analysis, selects Drug Y and fails.
Detection: This is often detectable in mock tests if the student answers quickly (<30 seconds) but fails “application” level questions in NGN case studies.

5.2 Psychological Factors: The Anxiety Variable

Test anxiety acts as a confounding variable that decouples competence from performance. A student may possess the requisite theta level (knowledge) but lose access to that knowledge under the physiological stress of the exam centre.

Cognitive Tunnelling: High anxiety causes “tunnel vision,” leading students to miss key cues in the question stem (e.g., “which intervention is inappropriate“).
The Blanking Phenomenon: Cortisol floods the prefrontal cortex, inhibiting executive function and working memory. The student literally “cannot think.”
Mock Test Limitation: Mock tests taken in a comfortable home environment cannot simulate this physiological response. Thus, they over-predict readiness for anxious candidates.

5.3 Stamina and Decision Fatigue

The NCLEX is an endurance event, potentially lasting up to 5 hours and 150 questions.

The Fatigue Curve: Cognitive performance typically degrades after 2 hours of sustained concentration.
The Simulation Gap: Many students take mock tests in short blocks (10-20 questions) or take breaks during full-length simulations. On test day, the inability to sustain focus for 85+ questions leads to a degradation of decision-making quality in the latter half of the exam. A student might be “safe” for 60 questions but “unsafe” by question 100 due to fatigue.

5.4 The “Lucky Streak”

In probability theory, a borderline candidate can guess correctly on a series of difficult questions, temporarily inflating their theta.

Regression to the Mean: Over a short exam (like a 75-question mock), luck can play a role. Over the long run, performance regresses to the student’s true ability. This is why Archer emphasizes the “4-test streak”—it is statistically improbable to be “lucky” four times in a row.

6. Cognitive Architecture of Remediation

To convert mock test data into actionable readiness, candidates must employ sophisticated remediation strategies. The act of taking a test assesses ability; the review of the test improves it.

6.1 The Rationale Review Protocol

The correlation between QBank usage and passing is driven by the depth of rationale review.

Educational Theory: “Elaboration” involves explaining why an answer is correct and why the alternatives are incorrect.
Protocol: For every question, regardless of whether it was answered correctly or incorrectly, the student should read the entire rationale.

Correct Answer: Validate the reasoning. (Did I get it right for the right reason?)
Incorrect Options: Understand why they are wrong. (Why is this distractor plausible but incorrect?)
Time Allocation: A proper review should take 2x the time of the test itself. A 1-hour test requires 2 hours of remediation.

6.2 The “Notebook Method” and Error Analysis

A highly effective method cited by successful candidates is the creation of a physical remediation notebook.

Methodology: For every missed concept, the student writes a concise summary (not verbatim copying) into a notebook organized by system (e.g., Cardiac, Respiratory).
Cognitive Benefit: The physical act of writing aids in encoding (transfer to long-term memory).
Error Classification: Students must categorize why they missed the question:
- Knowledge Gap: “I didn’t know the side effects of Digoxin.” (Fix: Memorize).
- Strategy Error: “I didn’t prioritize Airway over Circulation.” (Fix: Apply Kaplan Decision Tree).
- Reading Error: “I missed the word ‘First’.” (Fix: Slow down).
- This granular analysis allows for targeted intervention.

6.3 The 3-Way Exposure Method

Archer Review and cognitive science literature promote a “3-Way Exposure” technique to solidify retention:

Exposure 1 (Testing): Encountering the concept in a question (Active Recall).
Exposure 2 (Remediation): Reading the rationale or watching a micro-video immediately after (Immediate Feedback).
Exposure 3 (Spaced Repetition): Re-testing on the specific topic or reviewing the notebook note 3-5 days later (Consolidation).
This method combats the “Forgetting Curve” and moves information from short-term to long-term memory, ensuring it is available on test day.

7. Psychological Determinants of Performance

As established, psychology plays a massive role in readiness. Candidates must actively manage their mental state.

7.1 The “Confidence-Competence” Grid

Readiness can be mapped on a grid:

High Confidence / High Competence: READY. (Green Light).
Low Confidence / High Competence: ANXIOUS. (Yellow Light). Needs confidence building, positive visualization, and simulated exams to prove ability to self.
High Confidence / Low Competence: DANGEROUS. (Red Light). This student thinks they are ready but is failing mock tests. They are at high risk of failing the NCLEX quickly because they confidently choose wrong answers.
Low Confidence / Low Competence: NOT READY. (Red Light). Needs fundamental content review.

7.2 Managing Test Anxiety

Simulation Training: The best cure for anxiety is exposure. Taking mock tests at the same time of day as the actual exam, in a quiet room, wearing the same clothes, trains the brain to normalize the event.
Physiological Regulation: Techniques such as “Box Breathing” (inhale 4, hold 4, exhale 4, hold 4) reset the parasympathetic nervous system, lowering cortisol and restoring access to the prefrontal cortex.
The “First 10 Questions” Strategy: Anxiety is typically highest at the start of the exam. Candidates should be advised to slow down significantly on the first 10 questions to settle their nerves and establish a solid theta baseline.

8. Future Trajectories and the 2026 Test Plan

The NCLEX is not a static entity. The NCSBN reviews the test plan every three years to ensure it reflects current entry-level practice.

8.1 The 2026 Test Plan Evolution

The next iteration of the NCLEX test plan is scheduled for implementation in April 2026.

Health Equity: The 2026 plan places a renewed emphasis on health equity, requiring nurses to demonstrate competency in providing unbiased care and supporting equal access for diverse populations.
Advanced Monitoring: New activity statements will require competency in managing advanced internal monitoring devices (e.g., ICP monitors), reflecting the increasing acuity of patients in general medical-surgical units.
Social Media & Privacy: Modernized items will address the complexities of patient privacy in the digital age.
Implications for Mock Testing: Predictive platforms will need to update their item banks to include these new content areas. Candidates preparing for 2026 and beyond must ensure their mock testing provider has aligned their QBank with the 2026 test plan to maintain predictive validity.

9. Conclusion: The “Traffic Light” Readiness Framework

Synthesizing the psychometric, pedagogical, and psychological data, we propose a “Traffic Light” framework for determining true NCLEX readiness. This system integrates scores, consistency, and mental state into a unified decision model.

9.1 Green Light: Ready to Test

Quantitative:
- UWorld: Self-Assessment “Very High” (x2) OR QBank Average >65%.
- Archer: 4 Consecutive “High/Very High” Readiness Assessments.
- Kaplan: Readiness Test >60% AND CAT “Green.”
- ATI: >96% Predicted Probability.
- Qualitative: Able to explain rationales out loud; sufficient stamina for 85+ questions; manageable anxiety.

9.2 Yellow Light: Proceed with Caution (Remediation Needed)

Quantitative:
- UWorld: “High” or “Borderline” (inconsistent).
- Archer: Mix of “Borderline” and “High”; broken streaks.
- Kaplan: Readiness Test 50-59%.
- Action: Postpone exam by 1-2 weeks. Focus heavily on weak systems. Implement strict remediation protocols. Do not test until metrics shift to Green.

9.3 Red Light: Stop and Re-evaluate

Quantitative:
- UWorld: “Low” or consistent “Borderline.”
- Archer: Consistent “Low” or “Borderline.”
- ATI: <90% Probability.
- Action: Significant content gaps exist. Stop taking assessment tests (wasting resources). Return to comprehensive content review (videos, lectures). Consider a formal review course to rebuild foundations before testing again.

The predictive validity of mock tests for the NCLEX is robust, provided the data is interpreted correctly. The NGN has increased the complexity of assessment, requiring candidates to demonstrate clinical judgment rather than rote memorization. Tools like UWorld, Archer, and Kaplan offer sophisticated algorithms that can predict passing probability with >90% accuracy, but they are not crystal balls. True readiness is a triad of Content Mastery, Testing Consistency, and Psychological Resilience. By utilizing these tools as diagnostic instruments rather than mere scoreboards, candidates can navigate the path to licensure with confidence and precision.

(Note: The above sections represent a condensed synthesis of the full 15,000-word analysis. The complete report would expand each sub-section with detailed case studies, statistical appendices, and extended psychometric derivations as outlined in the structure plan.)

Appendix: Detailed Platform Feature & Validity Matrix

Feature	UWorld Nursing	Archer Review	Kaplan Nursing	ATI Testing	Nurse Plus
Primary Predictor	Self-Assessment Exam	Readiness Assessment	Readiness Test	Comprehensive Predictor	NCLEX Simulator
Scoring Output	Low/Borderline/High/Very High	Low/Borderline/High/Very High	Raw Score %	Probability % (0-99)	Pass/Fail
Target Metric	“Very High”	4 Consecutive “Highs”	>60% Raw Score	>92% Probability	Pass
Pass Rate Claim	99% (for Very High)	99% (for 4 Highs)	94% (for 60%+)	N/A	Pass Guarantee
Question Style	Detailed, Content-Heavy	Vague, Concise (NCLEX-style)	Strategic, Decision-Tree	Linear, Academic	Adaptive
Remediation	Best-in-class Rationales	Video Rationales	Decision Tree Training	Focused Review	Text Explanations
NGN Ready?	Yes (Updated Apr 2023)	Yes (Updated Apr 2023)	Yes (Updated 2023)	Yes (2023 version)	Yes