K Knowledge HF-D7.2-002 cross-sector 11 skills
Basic statistical interpretation
Knows and interprets basic statistical concepts such as spread, correlation, confidence interval and significance and states what an outcome does and does not show.
How hrmforce measures this
- Assessment method
- Knowledge test · rho 0.40 (SD 0.13)
- hrmforce instrument
- Ability Scan, Knowledge test (client-specific)
- Competency (50-framework)
- Judgment
- Trainability
- high
- Demand outlook 2026 to 2030
- rising
Test of declarative job knowledge, usually assembled per client.
Behavioural anchors
| Level | Behaviour at this level |
|---|---|
| N1 Guided | Reads mean, spread and counts correctly from a table and checks what a percentage exactly represents. works under supervision and follows instruction · routine, one variable at a time · own task |
| N3 Proficient | Interprets correlations, confidence intervals and significance in a report and points out where cause and effect are not established. sets own approach and seeks input proactively · several variables, some ambiguity · own team or process |
| N5 Leading | Reviews the statistical basis of organisation wide decisions and trains others in the correct interpretation of results. sets the standard and the policy · strategic, under high uncertainty · organisation, value chain or profession |
N2 and N4 are deliberately not anchored. Raters place them between the anchors, following the O*NET convention.
Underlying skills
These skills inherit the assessment route and the behavioural anchors of this construct.
| T | Skill | Definition | Demand outlook 2026 to 2030 |
|---|---|---|---|
| K | Interpreting correlation Correlation | Explains what strength and direction of an association mean and why association does not prove causation. | rising |
| K | Explaining confidence intervals Confidence interval | Explains which uncertainty an interval around an estimate expresses and what its width says about the sample. | rising |
| K | Understanding significance and p values p value · Statistical significance | Explains what a p value does and does not say and why significant is not the same as important. | rising |
| K | Assessing effect size Effect size | Assesses how large a found difference is in practice using measures such as Cohens d and explained variance. | rising |
| K | Interpreting measures of spread Standard deviation · Variance | Reads standard deviation, variance and range and states what the spread says about the group. | stable |
| K | Choosing mean or median Central tendency | Chooses between mean, median and mode based on distribution and outliers and justifies that choice. | stable |
| K | Distinguishing levels of measurement Levels of measurement | Distinguishes nominal, ordinal and interval data and chooses matching operations and charts accordingly. | stable |
| K | Recognising distributions Distributions | Recognises normal, skewed and bimodal distributions in a histogram and states consequences for the analysis choice. | stable |
| K | Distinguishing sample and population Sampling | Explains how a sample is drawn and when outcomes do or do not generalise to the population. | stable |
| K | Separating causation from association Causal inference | Names confounders, selection effects and reverse causation as explanations alongside an observed relationship. | rising |
| K | Reading percentages and index figures Percentages | Works with percentage change, percentage points and index figures and avoids common calculation and interpretation errors. | stable |