Curriculum Framework for Statistical Thinking on InMside
In all school subjects, there is a need to integrate,
to a lesser or greater degree, data handling, datacollection and data analysis into regular schoolroutines and activities.
n Neyman-Pearson statistics is well-suited tomake decisions using hypothesis testing, toSubjects
evaluate the consistency of models withobserved sample data, and to makeinferences (e.g., about what population or
universe a sample represents).
NEYMAN-PEARSON STATISTICS
CodingIENCESCDataDATAsStatisticnetiv yec lit
bj bi
Su obaPrSs’TICye em
Ba or ATISeThSTNodSIA
hoeli
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LikBAnFormal andProbability of to deal with population forecasting, missing
Bayesianstatistics
iswell-suited
Modeling
Informal Inference
Variabilityand noisy data, comparison of multiple hypotheses, addressing multiple levelsof uncertainty, and analyzing large numbers of variables to predict outcomes.

APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 20212Curriculum Framework for Statistical Thinking on InMside
In all school subjects, there is a need to integrate,
to a lesser or greater degree, data handling, datacollection and data analysis into regular schoolroutines and activities.
n Neyman-Pearson statistics is well-suited tomake decisions using hypothesis testing, toSubjects
evaluate the consistency of models withobserved sample data, and to makeinferences (e.g., about what population or
universe a sample represents).
NEYMAN-PEARSON STATISTICS
CodingIENCESCDataDATAsStatisticnetiv yec lit
bj bi
Su obaPrSs’TICye em
Ba or ATISeThSTNodSIA
hoeli
YE
LikBAnFormal andProbability of to deal with population forecasting, missing
Bayesianstatistics
iswell-suited
Modeling
Informal Inference
Variabilityand noisy data, comparison of multiple hypotheses, addressing multiple levelsof uncertainty, and analyzing large numbers of variables to predict outcomes.

APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 20212Curriculum Framework for Statistical Thinking on InMside
In all school subjects, there is a need to integrate,
to a lesser or greater degree, data handling, datacollection and data analysis into regular schoolroutines and activities.
n Neyman-Pearson statistics is well-suited tomake decisions using hypothesis testing, toSubjects
evaluate the consistency of models withobserved sample data, and to makeinferences (e.g., about what population or
universe a sample represents).
NEYMAN-PEARSON STATISTICS
CodingSCIENCEDatanDATAsStatisticnetiv yec lit
bj bi
Su obaPrSs’TICye em
Ba or ATISeThSTNodSIAhoeli
YELikBAnFormal andProbability of to deal with population forecasting, missing
Bayesianstatistics
iswell-suited
Modeling
Informal Inference
Variability
and noisy data, comparison of multiple hypotheses, addressing multiple levelsof uncertainty, and analyzing large numbers of variables to predict outcomes.
Data science is well-suited to discover patterns or latent structure in large
volumes of unstructured data, to transform big data into actionable insights.
APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

2

Curriculum Framework for Statistical Thinking on InMside
Neyman-Pearson statistics (i.e., frequentist, classical,or orthodox statistics) is currently the common
approach to statistical practice at school level.
n Inference is a probabilistic prediction orgeneralization about an unknown quantity,
based on information contained in a sample,
Subjects
using (formal) or not (informal) significancetests and formal probabilistic statements.
n Modeling is the process of generating amathematical model (e.g., simple orNEYMAN-PEARSON STATISTICS
multiple linear regression models) to best
Formal andProbability ofdescribe and provide evidence of theInformal Inference Modeling
Variability
relationshiprelationship between an outcome andone or more explanatory variables.
Probability of variability (i.e., the p-value returned by a significance test) isthe probability of obtaining the same or more extreme data given both ahypothesis and a decision procedure.
CodingIENCESCDataDATAsStatisticnetiv yec lit
bj bi
Su obaPrSs’TICye em
Ba or ATISeThSTNodSIAhoeli
YELikBAn

APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

3

Curriculum Framework for Statistical Thinking on InMsideCoding

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Bayesian statistics is about assessing thesubjective probability of hypotheses andunknown parameters, using the rules ofprobability to combine data with priorinformation to yield inferences.

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SubjectsnBayes’ theorem is an application of
conditional probability to calculate
the probability that an event has tohappen, in given circumstances.

NEYMAN-PEARSON
Likelihood
is theSTATISTICS
probability ofFormal andProbability ofobtaining
the exact
evidenceor dataModeling
Informal Inference
Variability
obtained
given a hypothesis,
P(E|H).
Subjective probability is a person’s
perception of the likelihood of an eventto occur, P(H), based on personal beliefs, preferences and experience.
APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

4

Curriculum Framework for Statistical Thinking on InMsideCoding

IENCESCDatan

DA
TAs
St
atisticn

Data science is a multi-disciplinary field that uses
scientific methods, processes, algorithms, andcomputational infrastructure to extract knowledge
and insights from structured and unstructured data.

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LikBAnSubjects

nStatistics allows data scientists to distinguishbetween causation and correlation; toestablish methods for diagnosis, prediction,estimation and decision-making; and toquantify their degree of certainty.

NEYMAN-PEARSON STATISTICS

Data
science requires Probability
the processing,analysis and visualization of very large
Formal andofModelingamountsof data
throughengagement in the five phases of Statistical
Informal InferenceVariability
Thinking in the Era of Big Data for the Digital Economy.
Coding in a script language (e.g., R, Python or SQL) is essential for feeding thecomputer with the appropriate commands to handle and analyze data.
APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

5

Curriculum Framework for Statistical Thinking on InMside

APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

6

Curriculum Framework for Statistical Thinking on InMsidenWe must help our students to develop their statistical thinking beyond theboundaries of traditional analytics (e.g., PPDAC), and provide them withopportunities to engage in big data analytics (i.e., patterns and relationships
from data, questions, objectives, data mining, and designing).
APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

6

Curriculum Framework for Statistical Thinking on InMsideDevelopment Framework

Conceptual Frameworknnn

High SchoolCurriculum

Middle School
CurriculumPrimary SchoolCurriculum

EXEMPLAR APPLICATIONSAPEC-Tsukuba International Conference XV(Webinar by the Project InMside II)This conceptual framework identifies
what statistical fundamental ideas,
thinking processes and competencies
Society 4.0 (and the upcoming Society5.0) demands from people.
Statistical education must continue toevolve to prepare the next generationof statistical users with skills that crossthe boundaries of traditional statistics.
Based on these considerations, wewould like to propose the following
curriculum framework model to foster
in our students Statistical Thinking for
the Era of Digital Society.

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

7

Questions and Comments are Welcomed!

APEC-Tsukuba International Conference XV(Webinar by the Project InMside II)

Keynote Lecture 1: Reform Framework to Develop
Computational and Statistical Thinking

February 10, 2021

8