Glossary

Short definitions of the statistics and ChakataStat terms used throughout this guide. For "which test do I need?", see Choosing a procedure.

ChakataStat terms

Case — one row of the dataset; one observation (e.g. one participant).
Variable — one column; a measured attribute (e.g. Age).
.ckd — ChakataStat's native document: data plus all variable definitions in one file.
.ckdict — a standalone data dictionary: variable metadata without the data.
.cks — ChakataStat syntax: the readable command-stream script the syntax console reads and writes.
Measurement level (Measure) — how a variable is treated: Scale (true number), Ordinal (ordered categories), Nominal (unordered categories).
Value label — text shown for a stored code (e.g. 1 → Male).
User-missing value — a code that means "no answer", excluded from analysis.
Case semantics — Select (which cases), Weight (how much each counts) and Split (analyse per group); applied to every analysis.
Tool / command — the single named operation behind every menu action, shared by the syntax console, the AI assistant and MCP.

Mean / Median / Mode — average, middle value, most frequent value.
Standard deviation (SD) — typical spread around the mean.
Confidence interval (CI) — a range that, at a stated level (e.g. 95%), plausibly contains the true value.
Significance (p-value) — the probability of seeing data this extreme if the null hypothesis were true; small values are evidence against the null.
Alpha (α) — the significance threshold for a reject/retain decision (commonly 0.05).
One- vs two-tailed — whether the test looks for a difference in one specified direction or in either direction.
Null hypothesis — the "no effect / no difference" baseline a test evaluates.

Correlation — strength and direction of association (−1 to +1). Pearson is linear; Spearman and Kendall are rank-based.
Partial correlation — association between two variables controlling for others.
Regression — predicting an outcome from one or more predictors.
Coefficient (B) — the modelled effect of a predictor on the outcome.
Odds ratio / Exp(B) — in logistic regression, the multiplicative change in odds per unit of a predictor.
Hazard ratio — in Cox regression, the multiplicative change in the event rate per unit of a predictor.
R² — the share of the outcome's variance the model explains.

t-test — compares two means.
ANOVA — compares three or more means; the F statistic is its test.
Levene's test — tests whether groups have equal variances.
Post-hoc test — pairwise comparisons after a significant ANOVA (Bonferroni, Scheffé, Tukey HSD).
Chi-square (χ²) — tests association in a contingency table.
Nonparametric test — a test that does not assume normality, using ranks.

Factor analysis — finds latent factors behind many correlated variables; loadings show each variable's link to a factor; rotation (Varimax, Promax) makes them interpretable.
Cronbach's alpha — internal-consistency reliability of a set of items.
Cluster analysis — groups similar cases (k-means, hierarchical, two-step).
Discriminant analysis — predicts known group membership from predictors.
Censoring — in survival data, a case whose event had not occurred by the end of observation.
Kaplan-Meier — a model-free estimate of a survival curve; log-rank compares curves between groups.
ACF / PACF — autocorrelation and partial autocorrelation of a time series.
ARIMA / SARIMA — Box-Jenkins time-series models; SARIMA adds seasonal terms; both can forecast.