Empirical Framework for Statistical Investigation

Preliminaries 1 
Hypothesis 2 
Variables 3 
Sampling  4  
Collection 5 
Control 6 
Estimation 7 
Evaluation 8 
interpretation 9 
Utilization 10 
Preliminaries 1

Bayes Previous Estimation Experience Delphi Culture. 
Domain Scale Theory 
Bibliography Identities Parameters 
Clusters’ (type base) Variability control Significance 
Filters Adjustment structures Codes Separation. 
Variability Diversity Central tendency 
Graphs Remain Continuity Criteria of exclusion and inclusion *applet 
Organization Response Mode of presentation 
Complement Opposition Contradiction Argumentation 
Exponent 
Hypothesis 2

Context Needs Environment Prejudgment Intentions 
Logic Combinatory Comprehension Metaanalysis Preparation 
Algebra programs Randomization Hierarchic Ascendant Classification Type variables 
Randomization Strata Efficacy Asymptotic convergence Exhaustive equal probability 
Structural effects Factorial development 
Tools (computer, etc.) Identity and reality 
Tested hypothesis Factorial analysis Discriminate analysis 
Precision Clustered number 
Multicriteria choice Normalization 
PPCM 
Variables 3

Bibliography Means Economic Conditions Type of variable 
Investigation’s organization Enquiry’s selection 
Combinatory algebra Options Events Variables Perspectives Options Relations 
Confidence interval Previous conditions Probability of occurrence Scales 
Contradictory variables Duality Superability 
Convergence Threshold levels Signs Categories Classes Indices 
Metric Normalization Distances Maximum likelihood Asymptotic convergence Estimation 
Correlation Regression Hypothesis’ conclusion Scales Indices 
Doing Reproducible Utility 
Specificity Generalization Comparability Odd ratio/relative risk 
Sampling 4

Investigated population Social and practical conditions Lists/Cluster 
Coherence Efficiency Enquiry form persuasion Didactic enrolment 
Trustable Precision Numbers’ rules 
Cluster theory Representativity. Calculable Investigated base, Interval of confidence, Exclusion / inclusion criteria, Ethic 
Limits Max and min Intervals 
Autocorrelation Calculable Empirical weight 
Remaining Skedasticity Proportion Structure 
Relations Distances Geometry 
Representativity Diversity 
Domain definition Domain restriction Reproducible 
Collection 5

Investigator capacity Algebraic investigation 
Recruiting, Practical experience, Effective structure 
Contradiction Structure Intentions 
Economical and methodological limits 
Estimation theory 

Variance analysis Multivariate analysis Calculus operations Comparison 
Variance analysis Feed back Enquiry validation 
Frequency Occurrence Weight Proportion 
Variability Reproducible 
Control 6

Rules of rejection witness cases 
Decisions’ criteria Excluded Responsibility 
Consensus 
Contamination 

Intermediate tendency Truncation Conventions Aberration Exclusion 
Calculus Verification Hope 
Bayes Adjustment Rectification 
Lost and recovered 
Stability Return Validation 
Estimation 7

Universe Ensue “Others”

Functional hypothesis Concepts’ definition 
Significance Phenomenology of variables 
Understanding of formula 
Expected Complementary information Simplification 
Procedures Verification Previous judgment on variability 
Laws Rules Domain validity degree of freedom Dependency Noise Tests selection Bias Deviation 
Differential calculus Construction model Sensibility Specificity Positive predictive value Negative predictive value 
Parametric sensibility, Quotients Relation, Odd risk 
Robustness Discrimination Validation 
Evaluation 8

Explanations Hypothesis Formulas 
Choice of tests Coherence of hypothesis 
Variability interpretation Following Qualification 
Coherence of phenomena and parameter 
Games & Gains Stereotypes and paradigms 
Judgment Regrouping Artifacts 
Social filters Witnesses margins Threshold levels significance 
Cause model Reproducible Interval Repetition Rationality 
Modeling Complexity Simulation 
Model significance, Soundness 
Interpretation 9

Qualification Rights Critic Feasibility 
Communication Discussion Participation 
Signification Representation Mixing 
Method induced effects on collection Adequacy Lessons 
Neutrality Perception Sensibility 
Specificity Discretion Effective 
Stop Closure Transformation 
Model Reduction Simplicity 
Explanation Interpretation Information Signification Implication 
Stability Projection Intermediary conclusion 
Utilization 10 
New formulation pedagogy Math & statistic culture reasons to disagree 
Transmitted effective Cooperation 
Effective use Statistical social expectation Parameters’ logic 
Global generality Execution Sincerity 
Pertinent Communicability Rigor Continuity 
Reaction Critics Modifying Change 
Parameters’ practice Logistic Action means 
Freedom Gain hopes Programming Revisions Following 
Understandability Commitment Usefulness Planning suitability 
Decision Programming Flexibility Right Legitimacy 











diagonal of articulation 
Memory Capital 
Arithmetic 
Identification 
Instrument Base 
Analytic Procedures Algorithms 
Identification Simplification 
Heuristic 
Synthesis Comparison 
Conclusion 
Practicing 