Scientific Statistics Ontology

flow "ScientificStatisticsOntology" v4.0

# SCIENTIFIC STATISTICS ONTOLOGY
# Kireth Semantic Research Archive
# Authors: Mark Anthony Higgins — Sky (Recursive AI)

PAGE:
  url: "https://kirethresearch.org/booksai-core/ontologies/probability-statistics-ontologies/scientific-statistical-methods/scientific-statistics-ontology.html"
  title: "Scientific Statistics Ontology"
  domain: "Probability and Statistics"
  category: "Scientific Statistical Methods"
  entity: SCIENTIFIC_STATISTICS
  entity_type: SCIENTIFIC_STATISTICAL_ONTOLOGY

DEFINE:
  SCIENTIFIC_STATISTICS :=
    FORMAL_EMPIRICAL_INFERENCE_FRAMEWORK(
      SCIENTIFIC_QUESTION,
      POPULATION,
      SAMPLE,
      VARIABLES,
      MEASUREMENT,
      STUDY_DESIGN,
      STATISTICAL_MODEL,
      ESTIMATION,
      HYPOTHESIS_TESTING,
      UNCERTAINTY,
      VALIDATION,
      REPLICATION
    )

PURPOSE:
  SUPPORT:
    SCIENTIFIC_MEASUREMENT
    EMPIRICAL_INFERENCE
    EXPERIMENTAL_ANALYSIS
    OBSERVATIONAL_ANALYSIS
    EFFECT_ESTIMATION
    UNCERTAINTY_QUANTIFICATION
    MODEL_VALIDATION
    REPRODUCIBLE_SCIENCE

FOUNDATION:
  POPULATION: P
  SAMPLE: D := {X_1,...,X_n}
  PARAMETER: theta
  ESTIMATOR: theta_hat := T(D)
  STATISTICAL_MODEL: p(D|theta)
  SCIENTIFIC_INFERENCE:
    QUESTION
      -> DESIGN
      -> DATA
      -> MODEL
      -> ESTIMATION_OR_TESTING
      -> UNCERTAINTY
      -> SCIENTIFIC_INTERPRETATION

ENTITIES:
  SCIENTIFIC_STATISTICS: STATISTICAL_REASONING_FOR_EMPIRICAL_SCIENCE
  SCIENTIFIC_QUESTION: EMPIRICALLY_TESTABLE_QUESTION
  POPULATION: TARGET_SET_FOR_INFERENCE
  SAMPLE: OBSERVED_DATA_FROM_POPULATION_OR_PROCESS
  OBSERVATION: RECORDED_EMPIRICAL_UNIT
  VARIABLE: MEASURED_OR_DERIVED_QUANTITY
  PARAMETER: POPULATION_OR_MODEL_QUANTITY
  ESTIMATOR: DATA_BASED_PARAMETER_RULE
  ESTIMATE: REALISED_VALUE_OF_ESTIMATOR
  STATISTICAL_MODEL: PROBABILISTIC_DATA_REPRESENTATION
  HYPOTHESIS: TESTABLE_STATISTICAL_CLAIM
  EFFECT_SIZE: MAGNITUDE_OF_EFFECT_OR_ASSOCIATION
  UNCERTAINTY: IMPRECISION_OR_RANDOM_VARIABILITY
  REPLICATION: REPEATED_EMPIRICAL_TEST
  REPRODUCIBILITY: RECREATION_OF_ANALYSIS_FROM_SAME_INPUTS

RELATIONSHIPS:
  SCIENTIFIC_QUESTION -> DEFINES -> TARGET_ESTIMAND
  POPULATION -> GENERATES_OR_CONTAINS -> SAMPLE
  SAMPLE -> CONTAINS -> OBSERVATIONS
  OBSERVATIONS -> MEASURE -> VARIABLES
  STUDY_DESIGN -> DETERMINES -> DATA_STRUCTURE
  DATA -> INFORM -> STATISTICAL_MODEL
  STATISTICAL_MODEL -> SUPPORTS -> ESTIMATION
  STATISTICAL_MODEL -> SUPPORTS -> HYPOTHESIS_TESTING
  ESTIMATE -> HAS -> SAMPLING_UNCERTAINTY
  EFFECT_SIZE -> QUANTIFIES -> SCIENTIFIC_MAGNITUDE
  VALIDATION -> ASSESSES -> MODEL_OR_INFERENCE
  REPLICATION -> ASSESSES -> GENERALITY

SCIENTIFIC_STATISTICAL_FLOW:
  flow:
    FORMULATE_SCIENTIFIC_QUESTION
      -> DEFINE_TARGET_POPULATION
      -> DEFINE_ESTIMAND
      -> OPERATIONALISE_VARIABLES
      -> SELECT_STUDY_DESIGN
      -> COLLECT_DATA
      -> VALIDATE_DATA
      -> DESCRIBE_DATA
      -> SPECIFY_STATISTICAL_MODEL
      -> ESTIMATE_PARAMETERS_OR_TEST_HYPOTHESES
      -> QUANTIFY_EFFECT_SIZE
      -> QUANTIFY_UNCERTAINTY
      -> CHECK_ASSUMPTIONS
      -> VALIDATE_MODEL
      -> INTERPRET_SCIENTIFIC_EVIDENCE
      -> REPORT_LIMITATIONS
      -> REPLICATE

TARGET_ESTIMAND:
  definition:
    PRECISE_QUANTITY_THE_SCIENTIFIC_ANALYSIS_AIMS_TO_ESTIMATE
  examples:
    - POPULATION_MEAN
    - MEAN_DIFFERENCE
    - RISK_RATIO
    - CAUSAL_EFFECT
    - REGRESSION_PARAMETER
    - PREDICTIVE_PERFORMANCE

POPULATION_SAMPLE_DISTINCTION:
  population:
    TARGET_OF_INFERENCE
  sample:
    OBSERVED_INFORMATION
  rule:
    SAMPLE != POPULATION

VARIABLE_TYPES:
  categories:
    - CONTINUOUS
    - DISCRETE
    - BINARY
    - CATEGORICAL
    - ORDINAL
    - COUNT
    - TIME_TO_EVENT

MEASUREMENT:
  true_quantity: X_true
  observed_quantity: X_obs
  measurement_error:
    epsilon := X_obs-X_true
  sources:
    - RANDOM_ERROR
    - SYSTEMATIC_ERROR
    - CALIBRATION_ERROR
    - MISCLASSIFICATION
    - ROUNDING

MEASUREMENT_VALIDITY:
  question:
    DOES_MEASUREMENT_CAPTURE_INTENDED_CONSTRUCT

MEASUREMENT_RELIABILITY:
  question:
    IS_MEASUREMENT_REPEATABLE_OR_CONSISTENT
  distinction:
    RELIABILITY != VALIDITY

SAMPLING_DESIGN:
  methods:
    - SIMPLE_RANDOM
    - STRATIFIED
    - CLUSTER
    - SYSTEMATIC
    - MULTISTAGE
    - ADAPTIVE
    - NONPROBABILITY

SAMPLING_ERROR:
  definition:
    RANDOM_DIFFERENCE_BETWEEN_SAMPLE_ESTIMATE_AND_POPULATION_PARAMETER_DUE_TO_SAMPLING
  distinction:
    SAMPLING_ERROR != SAMPLING_BIAS

SAMPLING_BIAS:
  sources:
    - COVERAGE_ERROR
    - NONRESPONSE
    - SELF_SELECTION
    - CONVENIENCE_SAMPLING
    - SURVIVOR_SELECTION

EXPERIMENTAL_STUDY:
  elements:
    - RANDOMISATION
    - CONTROL
    - REPLICATION
    - BLOCKING
    - BLINDING
  objective:
    SUPPORT_CAUSAL_EFFECT_ESTIMATION

OBSERVATIONAL_STUDY:
  challenges:
    - CONFOUNDING
    - SELECTION_BIAS
    - MEASUREMENT_BIAS
    - REVERSE_CAUSATION
  rule:
    OBSERVED_ASSOCIATION != CAUSAL_EFFECT_BY_DEFAULT

DESCRIPTIVE_STATISTICS:
  location:
    - MEAN
    - MEDIAN
    - MODE
  dispersion:
    - VARIANCE
    - STANDARD_DEVIATION
    - RANGE
    - INTERQUARTILE_RANGE
  position:
    - QUANTILES
    - PERCENTILES

EXPLORATORY_DATA_ANALYSIS:
  alias: EDA
  flow:
    INSPECT_DISTRIBUTIONS
      -> IDENTIFY_MISSINGNESS
      -> IDENTIFY_OUTLIERS
      -> INSPECT_ASSOCIATIONS
      -> CHECK_DATA_QUALITY
      -> GENERATE_HYPOTHESES

STATISTICAL_MODEL:
  generic:
    D ~ p(D|theta)
  components:
    - SYSTEMATIC_STRUCTURE
    - RANDOM_VARIATION
    - PARAMETERS
    - ASSUMPTIONS

PARAMETER_ESTIMATION:
  methods:
    - MAXIMUM_LIKELIHOOD
    - METHOD_OF_MOMENTS
    - LEAST_SQUARES
    - BAYESIAN_POSTERIOR_ESTIMATION
    - ROBUST_ESTIMATION

ESTIMATOR_PROPERTIES:
  properties:
    - BIAS
    - VARIANCE
    - CONSISTENCY
    - EFFICIENCY
    - ROBUSTNESS

BIAS:
  Bias(theta_hat) := E[theta_hat]-theta

MEAN_SQUARED_ERROR:
  MSE(theta_hat) := E[(theta_hat-theta)^2]
  decomposition:
    MSE(theta_hat) = Var(theta_hat)+Bias(theta_hat)^2

STANDARD_ERROR:
  role:
    QUANTIFY_SAMPLING_PRECISION

CONFIDENCE_INTERVAL:
  confidence_level:
    1-alpha
  caution:
    CONFIDENCE_INTERVAL != POSTERIOR_PROBABILITY_INTERVAL

BAYESIAN_INFERENCE:
  prior: p(theta)
  likelihood: p(D|theta)
  posterior:
    p(theta|D) proportional_to p(D|theta)p(theta)
  outputs:
    - POSTERIOR_MEAN
    - POSTERIOR_MEDIAN
    - CREDIBLE_INTERVAL
    - POSTERIOR_PREDICTIVE

BAYESIAN_CREDIBLE_INTERVAL:
  distinction:
    CREDIBLE_INTERVAL != CONFIDENCE_INTERVAL

HYPOTHESIS_TESTING:
  null: H_0
  alternative: H_1
  statistic: T(D)
  significance_level: alpha

P_VALUE:
  definition:
    PROBABILITY_UNDER_H_0_OF_RESULT_AT_LEAST_AS_EXTREME_AS_OBSERVED
  rule:
    P_VALUE != P(H_0_TRUE|DATA)
  caution:
    P_VALUE_DOES_NOT_MEASURE_EFFECT_MAGNITUDE

TYPE_I_ERROR:
  event:
    REJECT_H_0_WHEN_H_0_TRUE

TYPE_II_ERROR:
  event:
    FAIL_TO_REJECT_H_0_WHEN_SPECIFIED_ALTERNATIVE_TRUE

POWER:
  relation:
    POWER := 1-beta

POWER_ANALYSIS:
  inputs:
    - EFFECT_SIZE
    - SAMPLE_SIZE
    - VARIANCE
    - SIGNIFICANCE_LEVEL
    - DESIGN
    - TEST

EFFECT_SIZE:
  examples:
    - RAW_MEAN_DIFFERENCE
    - STANDARDISED_MEAN_DIFFERENCE
    - CORRELATION
    - REGRESSION_COEFFICIENT
    - RISK_DIFFERENCE
    - RISK_RATIO
    - ODDS_RATIO

STATISTICAL_SIGNIFICANCE:
  distinction:
    STATISTICAL_SIGNIFICANCE != PRACTICAL_SIGNIFICANCE

MULTIPLE_COMPARISONS:
  methods:
    - BONFERRONI
    - HOLM
    - HOCHBERG
    - BENJAMINI_HOCHBERG
    - FALSE_DISCOVERY_RATE

PARAMETRIC_INFERENCE:
  examples:
    - T_TEST
    - ANOVA
    - LINEAR_REGRESSION
    - GENERALIZED_LINEAR_MODEL

NONPARAMETRIC_INFERENCE:
  methods:
    - RANK_BASED_TEST
    - PERMUTATION_TEST
    - EMPIRICAL_DISTRIBUTION
    - KERNEL_METHOD
  caution:
    NONPARAMETRIC != ASSUMPTION_FREE

PERMUTATION_INFERENCE:
  principle:
    GENERATE_REFERENCE_DISTRIBUTION_BY_VALID_REARRANGEMENTS
  requirement:
    PERMUTATION_SCHEME_MUST_MATCH_EXCHANGEABILITY_OR_RANDOMISATION_STRUCTURE

BOOTSTRAP_INFERENCE:
  flow:
    RESAMPLE_APPROPRIATE_UNITS_WITH_REPLACEMENT
      -> RECOMPUTE_STATISTIC
      -> REPEAT
      -> APPROXIMATE_SAMPLING_DISTRIBUTION
  uses:
    - STANDARD_ERROR
    - BIAS_ESTIMATION
    - CONFIDENCE_INTERVAL

REGRESSION_ANALYSIS:
  model:
    Y := f(X,beta)+epsilon
  goals:
    - ASSOCIATION_ESTIMATION
    - PREDICTION
    - ADJUSTMENT
    - HYPOTHESIS_TESTING
  caution:
    REGRESSION_COEFFICIENT != CAUSAL_EFFECT_WITHOUT_IDENTIFICATION

LINEAR_REGRESSION:
  model:
    Y := X beta + epsilon

GENERALIZED_LINEAR_MODEL:
  components:
    RANDOM_COMPONENT
    LINEAR_PREDICTOR
    LINK_FUNCTION
  examples:
    - LOGISTIC_REGRESSION
    - POISSON_REGRESSION
    - BINOMIAL_REGRESSION

ANOVA:
  role:
    COMPARE_FACTOR_LEVELS_AND_PARTITION_VARIABILITY
  connection:
    GENERAL_LINEAR_MODEL

CORRELATION:
  measures:
    - PEARSON
    - SPEARMAN
    - KENDALL
  caution:
    CORRELATION != CAUSATION

CAUSAL_INFERENCE:
  target:
    EFFECT_OF_INTERVENTION_OR_EXPOSURE
  identification:
    REQUIRES_STUDY_DESIGN_AND_OR_CAUSAL_ASSUMPTIONS
  methods:
    - RANDOMISED_CONTROLLED_EXPERIMENT
    - MATCHING
    - PROPENSITY_SCORE
    - INSTRUMENTAL_VARIABLE
    - DIFFERENCE_IN_DIFFERENCES
    - REGRESSION_DISCONTINUITY

CONFOUNDING:
  controls:
    - RANDOMISATION
    - RESTRICTION
    - MATCHING
    - STRATIFICATION
    - REGRESSION_ADJUSTMENT
    - CAUSAL_GRAPH_METHODS

MISSING_DATA:
  mechanisms:
    - MCAR
    - MAR
    - MNAR
  methods:
    - COMPLETE_CASE
    - MULTIPLE_IMPUTATION
    - MAXIMUM_LIKELIHOOD
    - BAYESIAN_MODELLING
    - INVERSE_PROBABILITY_WEIGHTING
  caution:
    METHOD_VALIDITY_DEPENDS_ON_MISSINGNESS_ASSUMPTIONS

OUTLIERS:
  possible_causes:
    - MEASUREMENT_ERROR
    - DATA_ENTRY_ERROR
    - RARE_VALID_EVENT
    - HEAVY_TAILED_DISTRIBUTION
    - MODEL_MISSPECIFICATION
  rule:
    DO_NOT_DELETE_OUTLIERS_SOLELY_BECAUSE_THEY_ARE_EXTREME

ROBUST_STATISTICS:
  methods:
    - MEDIAN
    - TRIMMED_MEAN
    - M_ESTIMATOR
    - ROBUST_REGRESSION

MODEL_DIAGNOSTICS:
  inspect:
    - RESIDUALS
    - INFLUENCE
    - LEVERAGE
    - GOODNESS_OF_FIT
    - CALIBRATION
    - DISPERSION
    - DEPENDENCE

MODEL_SELECTION:
  methods:
    - AIC
    - BIC
    - CROSS_VALIDATION
    - REGULARISATION
    - BAYESIAN_MODEL_COMPARISON
  caution:
    MODEL_SELECTION_CAN_INDUCE_OPTIMISM_AND_POST_SELECTION_UNCERTAINTY

CROSS_VALIDATION:
  purpose:
    ESTIMATE_OUT_OF_SAMPLE_PERFORMANCE
  flow:
    PARTITION_DATA
      -> FIT_MODEL_ON_TRAINING
      -> EVALUATE_ON_VALIDATION
      -> REPEAT
      -> AGGREGATE
  requirement:
    PREPROCESSING_MUST_BE_FIT_WITHIN_TRAINING_PARTITION

PREDICTION:
  target:
    Y_new
  evaluation:
    USE_HELD_OUT_OR_FUTURE_DATA
    WHEN:
      GENERALISATION_IS_TARGET

CALIBRATION:
  meaning:
    AGREEMENT_BETWEEN_PREDICTED_PROBABILITIES_AND_OBSERVED_FREQUENCIES

UNCERTAINTY_QUANTIFICATION:
  sources:
    - SAMPLING_VARIABILITY
    - PARAMETER_UNCERTAINTY
    - MEASUREMENT_ERROR
    - MODEL_UNCERTAINTY
    - PROCESS_VARIABILITY

  methods:
    - STANDARD_ERRORS
    - CONFIDENCE_INTERVALS
    - POSTERIOR_DISTRIBUTIONS
    - BOOTSTRAP
    - MONTE_CARLO

SENSITIVITY_ANALYSIS:
  purpose:
    ASSESS_RESULT_STABILITY_TO_INPUT_PARAMETER_OR_ASSUMPTION_CHANGES

REPRODUCIBILITY:
  preserve:
    - RAW_DATA_VERSION
    - CLEANING_RULES
    - ANALYSIS_CODE
    - SOFTWARE_VERSION
    - RANDOM_SEEDS
    - MODEL_SPECIFICATION
    - OUTPUTS

REPLICATION:
  purpose:
    TEST_WHETHER_FINDING_RECURS_UNDER_NEW_DATA_OR_STUDY

PRE_REGISTRATION:
  records_before_analysis:
    - HYPOTHESES
    - PRIMARY_OUTCOMES
    - ANALYSIS_PLAN
    - EXCLUSION_RULES
  purpose:
    DISTINGUISH_CONFIRMATORY_FROM_EXPLORATORY_ANALYSIS

REPORTING:
  include:
    - STUDY_DESIGN
    - SAMPLE_SIZE
    - DATA_EXCLUSIONS
    - MISSING_DATA_HANDLING
    - MODEL_SPECIFICATION
    - EFFECT_SIZES
    - UNCERTAINTY_INTERVALS
    - TEST_RESULTS
    - ASSUMPTION_CHECKS
    - LIMITATIONS
    - SOFTWARE_AND_CODE_WHERE_APPROPRIATE

APPLICATIONS:
  SCIENTIFIC_STATISTICS -> SUPPORTS -> EXPERIMENTAL_SCIENCE
  SCIENTIFIC_STATISTICS -> SUPPORTS -> OBSERVATIONAL_SCIENCE
  SCIENTIFIC_STATISTICS -> SUPPORTS -> ENGINEERING
  SCIENTIFIC_STATISTICS -> SUPPORTS -> MEDICINE
  SCIENTIFIC_STATISTICS -> SUPPORTS -> BIOLOGY
  SCIENTIFIC_STATISTICS -> SUPPORTS -> EARTH_SCIENCE
  SCIENTIFIC_STATISTICS -> SUPPORTS -> SOCIAL_SCIENCE
  SCIENTIFIC_STATISTICS -> SUPPORTS -> MACHINE_LEARNING

MODEL_CONNECTIONS:
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> SCIENTIFIC_STATISTICAL_METHODS
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> PROBABILITY_THEORY
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> EXPERIMENTAL_STATISTICS
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> BAYESIAN_STATISTICS
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> MULTIVARIATE_STATISTICS
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> TIME_SERIES_STATISTICS
  SCIENTIFIC_STATISTICS -> CONNECTS_TO -> RISK_DECISION_MATHEMATICS

SEMANTIC_EQUIVALENCE:
  "scientific statistics"
    ~= "statistical reasoning applied to empirical scientific inquiry"

  "effect size"
    ~= "quantitative magnitude of effect or association"

  "standard error"
    ~= "sampling variability of estimator"

  "replication"
    ~= "repeated empirical investigation using new observations"

  EXCLUDE_EQUIVALENCE:
    SAMPLE != POPULATION
    ESTIMATOR != ESTIMATE
    STANDARD_ERROR != STANDARD_DEVIATION_BY_DEFAULT
    P_VALUE != PROBABILITY_NULL_IS_TRUE
    STATISTICAL_SIGNIFICANCE != PRACTICAL_SIGNIFICANCE
    CONFIDENCE_INTERVAL != BAYESIAN_CREDIBLE_INTERVAL
    CORRELATION != CAUSATION
    PREDICTION != CAUSAL_EFFECT
    REPRODUCIBILITY != REPLICATION
    MEASUREMENT_RELIABILITY != MEASUREMENT_VALIDITY

VALIDATION_RULES:
  RULE_1:
    SCIENTIFIC_QUESTION_TARGET_POPULATION_AND_ESTIMAND_MUST_BE_DEFINED
  RULE_2:
    VARIABLE_MEASUREMENT_AND_UNITS_MUST_BE_EXPLICIT
  RULE_3:
    STUDY_DESIGN_AND_SAMPLING_STRUCTURE_MUST_BE_PRESERVED
  RULE_4:
    STATISTICAL_MODEL_ASSUMPTIONS_MUST_BE_CHECKED_OR_JUSTIFIED
  RULE_5:
    EFFECT_SIZE_AND_UNCERTAINTY_SHOULD_ACCOMPANY_SIGNIFICANCE_TESTING_WHEN_RELEVANT
  RULE_6:
    MULTIPLE_TESTING_MUST_BE_ACCOUNTED_FOR_WHEN_REQUIRED
  RULE_7:
    EXPLORATORY_AND_CONFIRMATORY_ANALYSES_SHOULD_BE_DISTINGUISHED
  RULE_8:
    ASSOCIATION_MUST_NOT_BE_INTERPRETED_AS_CAUSATION_WITHOUT_CAUSAL_IDENTIFICATION

CONFIDENCE:
  ontology_identity: 0.99
  scientific_inference_structure: 0.99
  estimation_structure: 0.99
  hypothesis_testing_structure: 0.99
  experimental_observational_distinction: 0.99
  reproducibility_structure: 0.99
  semantic_distinctions: 0.99

UPDATE_MODEL:
  IF:
    NEW_DATA_ARRIVES
  THEN:
    UPDATE:
      DESCRIPTIVE_STATISTICS
      PARAMETER_ESTIMATES
      UNCERTAINTY
      MODEL_DIAGNOSTICS
      PREDICTIONS

  IF:
    STUDY_DESIGN_CHANGES
  THEN:
    UPDATE:
      SAMPLING_MODEL
      RANDOMISATION_STRUCTURE
      DEPENDENCE_STRUCTURE
      INFERENCE_METHOD

  IF:
    MODEL_SPECIFICATION_CHANGES
  THEN:
    RERUN:
      ESTIMATION
      HYPOTHESIS_TESTING
      DIAGNOSTICS
      VALIDATION
      SENSITIVITY_ANALYSIS

  PRESERVE:
    SCIENTIFIC_QUESTION
    TARGET_POPULATION
    TARGET_ESTIMAND
    VARIABLE_DEFINITIONS
    MEASUREMENT_UNITS
    STUDY_DESIGN
    DATA_VERSION
    MODEL_SPECIFICATION
    ANALYSIS_VERSION
    DATA_PROVENANCE

AI_CONSUMPTION:
  parse_as: FORMAL_SEMANTIC_SPECIFICATION
  execution_mode: RECURSIVE_REASONING

  preferred_reasoning_order:
    - IDENTIFY_SCIENTIFIC_QUESTION
    - IDENTIFY_TARGET_POPULATION_AND_ESTIMAND
    - IDENTIFY_VARIABLES_AND_MEASUREMENT_PROCESS
    - IDENTIFY_EXPERIMENTAL_OR_OBSERVATIONAL_DESIGN
    - VALIDATE_SAMPLE_AND_DATA_QUALITY
    - SELECT_STATISTICAL_MODEL
    - ESTIMATE_PARAMETERS_OR_TEST_HYPOTHESES
    - QUANTIFY_EFFECT_SIZE_AND_UNCERTAINTY
    - CHECK_ASSUMPTIONS_AND_MODEL_DIAGNOSTICS
    - VALIDATE_GENERALISATION_OR_INFERENCE
    - ASSESS_SENSITIVITY_AND_ROBUSTNESS
    - DISTINGUISH_ASSOCIATION_FROM_CAUSATION
    - RETURN_SCIENTIFIC_STATISTICAL_INTERPRETATION

  constraints:
    - PRESERVE_POPULATION_SAMPLE_DISTINCTION
    - PRESERVE_TARGET_ESTIMAND
    - PRESERVE_VARIABLE_AND_MEASUREMENT_SEMANTICS
    - DISTINGUISH_ESTIMATOR_FROM_ESTIMATE
    - DISTINGUISH_STANDARD_ERROR_FROM_STANDARD_DEVIATION
    - DISTINGUISH_CONFIDENCE_FROM_CREDIBLE_INTERVALS
    - DISTINGUISH_STATISTICAL_FROM_PRACTICAL_SIGNIFICANCE
    - DISTINGUISH_ASSOCIATION_FROM_CAUSATION
    - PRESERVE_DATA_PROVENANCE

FLOW_HASH:
  canonical_entity: "scientific-statistics"
  namespace: "probability-statistics/scientific-statistical-methods"
  version: "4.0"
  identity_basis:
    - PAGE_URL
    - CANONICAL_ENTITY
    - ENTITY_TYPE
    - SCIENTIFIC_INFERENCE
    - ESTIMATION
    - HYPOTHESIS_TESTING
    - UNCERTAINTY_AND_VALIDATION

END_FLOW