General Mental Ability Test

Numerical reasoning evaluates a candidate's ability to work with numbers, interpret data, and apply mathematical concepts to solve problems. This sub-skill is important as it assesses a candidate's quantitative reasoning ability, which is valuable in roles that involve financial analysis, data analysis, budgeting, and other numerical tasks.

Abstract reasoning assesses a candidate's ability to identify patterns, solve problems with limited information, and think conceptually. This sub-skill is crucial as it reflects a candidate's ability to think creatively, make connections between ideas, and apply logical reasoning to unfamiliar situations.

Logical reasoning evaluates a candidate's ability to analyze arguments, identify inconsistencies, and draw valid conclusions. This sub-skill is essential as it indicates a candidate's ability to think critically, evaluate evidence, and make sound judgments, which are important in problem-solving, decision-making, and strategic planning.

Spatial reasoning assesses a candidate's ability to mentally manipulate shapes, visualize objects, and understand spatial relationships. This sub-skill is crucial as it reflects a candidate's aptitude for tasks that require visualizing designs, interpreting maps or diagrams, and solving problems in fields such as engineering, architecture, and graphic design.

Analytical thinking evaluates a candidate's ability to break down complex problems into their component parts, analyze information, and identify solutions. This sub-skill is important as it demonstrates a candidate's ability to approach problems systematically, identify patterns, and think critically to arrive at logical solutions. It is valuable in roles that require problem-solving, data analysis, and decision-making.

Data Interpretation assesses how well a candidate extracts, transforms, and reasons with information presented in tables, charts, and summaries. Items probe the ability to compute ratios, percentages, deltas, margins, and weighted rates, while spotting outliers and trends. Strong performance indicates the candidate can separate signal from noise, apply the right operation (not just any operation), and contextualize results for decisions. In real roles, this skill underpins reporting accuracy, KPI ownership, A/B test reading, and quick, defensible calls when only partial data or time-constrained dashboards are available.

Critical Thinking evaluates a candidate’s ability to analyze claims, detect assumptions, identify logical fallacies, and distinguish correlation from causation. Prompts simulate real business inferences—policy changes, marketing lifts, survey results—requiring candidates to seek counterevidence, consider confounders, and choose the most defensible conclusion. The skill matters because poor inference leads to costly missteps: shipping the wrong variant, misreading customer sentiment, or extrapolating from biased samples. Strong critical thinkers pressure-test hypotheses, value base rates, and ask for the “missing variable,” improving judgment quality across strategy, product, analytics, and operations.

Working Memory measures the ability to temporarily hold and manipulate information under rules (e.g., N-back, mental arithmetic, rule-switch tasks). Items require tracking sequences, updating intermediate values, and inhibiting incorrect but salient responses. It’s important because many day-to-day tasks—debugging, multi-step calculations, interpreting changing instructions—depend on keeping the right facts active while discarding distractions. High working-memory capacity correlates with fewer execution errors, better adherence to procedures, and faster recovery when requirements shift mid-task, ultimately supporting reliability in roles that juggle multiple inputs and evolving constraints.

Processing Speed & Accuracy captures how quickly and precisely candidates can execute low-level comparisons and validations under time pressure. Typical items include near-duplicate code/string checks, digit/character discrimination, and simple numeric judgments. The construct matters because many workflows depend on fast, error-free screening before higher-level reasoning begins—catching a transposed character, verifying a figure, or scanning lists for anomalies. Faster throughput with maintained accuracy reduces rework and bottlenecks, enabling teams to scale quality checks, keep SLAs, and maintain data hygiene without sacrificing downstream analytical or operational rigor.

Applied Quantitative Reasoning evaluates practical math used in real decisions: percentages, proportions, rates, compounding, unit conversions, and mixtures. Items emphasize setup as much as calculation—choosing the right formula, sequencing operations correctly, and sanity-checking results. This skill is vital for pricing, forecasting, budgeting, performance analysis, and experiment readouts. Strong performers translate verbal problems into numeric models, avoid common percentage traps (e.g., non-additivity), and communicate the “so what” clearly. Organizations benefit through fewer financial errors, more trustworthy dashboards, and quicker, defensible choices grounded in sound arithmetic.

Problem Solving & Heuristics measures structured reasoning under constraints: sequencing tasks, choosing efficient strategies, using approximations, and recognizing patterns that shortcut brute force. Items include order/precedence logic, back-of-the-envelope estimates, and “best method” selections. Its importance lies in speed-to-solution when perfect information is unavailable: picking a workable plan, narrowing the search space, and knowing when a heuristic (e.g., rounding, factorization) is “good enough.” Strong problem solvers conserve time and cognitive load, resolve blockers independently, and improve team velocity without sacrificing correctness where it truly matters.