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Table of content
I. Introduction............................................................................................................................... 1
II. Data, information and knowledge...........................................................................................1
2.1. The concept of data, information and knowledge..........................................................1
2.2. The process of transforming data into information and knowledge...............................1
2.3. Example........................................................................................................................1
III. Data evaluation using ,ethods of analysis..............................................................................1
3.1. Descriptive data analysis...............................................................................................1
3.2. Exploratory data analysis...............................................................................................1
3.3. Confirmatory data analysis............................................................................................1
3.4. Evaluate data from variety of source.............................................................................1
3.5. “Critically evaluate” “ in application”.............................................................................1
IV. Analyze and evaluate data using statistical methods............................................................1
4.1. Qualitative and quantitative data..................................................................................1
4.2. Statiscal methods..........................................................................................................2
4.2.1. Descriptive statistics...............................................................................................2
4.2.2. Inforential statistics................................................................................................2
4.2.3. Measuring association............................................................................................2
4.2.4. The differences in application between Descriptive statistics, Inferential statistics &
Measuring association......................................................................................................2
4.3. Analyze and evaluate qualitative data...........................................................................3
4.4. Analyze and evaluate quantitative data.........................................................................3
V. Conclusion.................................................................................................................................3
I. Introduction
In the age of data-driven decision making, the ability to understand, analyze, and interpret data
has become a fundamental skill across various disciplines. Probability and statistics serve as
essential tools in transforming raw data into meaningful information and actionable knowledge.
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This subject provides a structured approach to evaluate uncertainty, quantify relationships
between variables, and support evidence-based reasoning.
This report introduces the foundational concepts of data, information, and knowledge, and
outlines how data can be systematically transformed through statistical methods. It also
explores multiple layers of data analysis—including descriptive, exploratory, and confirmatory
approaches—to extract insights from a variety of data sources. Furthermore, the report
compares and contrasts statistical methods such as descriptive statistics, inferential statistics,
and measures of association, providing critical evaluation of their applications in practice.
By integrating both qualitative and quantitative perspectives, this study aims to equip learners
with the ability to analyze and evaluate data rigorously. Ultimately, the course in probability
and statistics not only enhances analytical thinking but also lays the groundwork for informed
decision-making in fields ranging from business and economics to healthcare and social
sciences.
II. Data, information and knowledge
2.1. The concept of data, information and knowledge
Data: Data is a term used to refer to various sets of information and facts, which are collected,
stored, processed and shared to obtain useful information. Data can exist in many different
forms such as letters, numbers written on text, or bytes in computer memory, or events
recorded in human memory. (Hue, 2025)
Information: Information is the output from analyzing, contextualizing, structuring, interpreting
or otherwise processing data. Information imparts meaning and value to data. It facilitates
understanding, communication and learning, and is a key element in system design and
strategic planning, as well as in problem solving and decision making. Information brings
context to data, turning what would otherwise be meaningless content into something
understandable and usable. (Sheldon, 2024)
Knowledge: Knowledge is “information in context to create actionable understanding”.
Knowledge is the most cherished remedy for complexity and uncertainty. It is a higher level of
abstraction that resides in the human mind. It is broader, richer, and more elusive than data or
information. People seek knowledge because it helps them succeed in their work. Knowledge is
actionable (relevant) information that is available in the right format, at the right time, and in
the right place for success. It is important to remember that knowledge has different meanings
depending on the discipline in which it is used. In the context of knowledge management,
knowledge is “human understanding of a specialized area of interest that has been acquired
through learning and experience”. In a way, it is information that has been enhanced by
experience provided by the validation process over time. It is based on learning, thinking, and
familiarity with the problem area within a department, division, or across the entire company.
The focus is on sustainable competitive advantage.(Chadha, 2021)
2.2. The process of transforming data into information and knowledge
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Nature of Data
Data is raw, unorganized facts or figures that are collected and stored. Data can be in the form
of numbers, text, images or any other type of input data.
Data itself has no context and meaning. It is the most basic form of representation and needs
further processing to become useful. (Spilker, 2024)
Transforming data into information
Define goals and problems to solve: This is the first and most important step, where you define
what needs to be achieved or what problems need to be analyzed. From here, goals or KPIs
(Key Performance Indicators) are set based on the identified problems.
Data collection: This involves collecting data from various sources, in line with the set goals.
Therefore, data tracking will ensure that the data needed for analysis is collected
systematically.
Data Processing and Verification: In this step, the data will be screened so that only accurate
and relevant data is used for analysis. This step will support better Data Mapping to understand
the structure and connections between different data.
Data Analysis: Using statistical and analytical methods to draw information and insights from
processed data. This is the stage where data analysis focuses on revealing patterns, trends and
relationships, providing insights for subsequent strategies.
Visualization and interpretation of results: Finally, the analyzed data will be presented in a
graphical form for easy understanding, making data-driven decision making smarter and more
effective. This visualization not only helps to better understand the analysis results but also aids
in making accurate strategic decisions. (Hanh, 2024)
The nature of information
Information is a form of data that has been processed and organized. It is data that has been
analyzed, structured, and given context. Information provides meaning and can be used to
answer questions or make decisions. This is the result of data being transformed into a more
meaningful and useful state. (Spilker, 2024)
Transforming into knowledge
Information creates a framework for developing knowledge, knowledge drives good decision-
making, and wisdom upholds the culture to sustain decisions. A focused effort on the
transformation of information to knowledge and knowledge to wisdom is the soul of a
successful knowledge management system. Whether your knowledge management system is
set up to organize e-mail messages or to integrate the needs of a facility's complex web of
users, the fundamentals are basically the same-the ability to organize, interpret, communicate,
and make decisions using multiple sources and bits of information.
Knowledge is information combined with experience, context, interpretation, and reflection. It
is not just the sum of what is known that is important, it is the ability to both create and
effectively use knowledge that will distinguish successful organizations in the future. Knowledge
creation is the act of taking relatively random data from across a broad spectrum and
translating that data into a meaningful insightful context.
Knowledge is more than the accumulation of random facts and data relevant to a particular
topic. Knowledge is not merely information. It is the transformation of information into a
meaningful and insightful context through study, investigation, observation, and experience.
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Wisdom is the ability to sustain and integrate the collected knowledge through developed
cultural norms and common understandings.
Transforming information to useful knowledge that will lead to good decision making is an
awesome task. The use of technology as a storage and retrieval tool is widely discussed as the
answer to this challenge. New systems and platforms are constantly being introduced providing
greater integration, increased storage, and faster retrieval causing greater frustration,
increased confusion, and faster tempers for those responsible for these efforts.( no date)
2.3. Example
According to data from the General Statistics Office of Vietnam (Gso.gov.vn, 2024), revenue
from accommodation, food and beverage and travel services in the first quarter of 2024 grew
strongly. Specifically, revenue from accommodation and food services reached VND 174.9
trillion, up 13.4% over the same period; while revenue from travel and tourism services reached
VND 14.1 trillion, up 46.3%. At this stage, the above numbers are considered raw data, because
when standing alone, they do not fully reflect any specific meaning or trend.
To convert data into information, it is necessary to put them in a specific context. In this case,
the context is: “Revenue from accommodation, food and beverage and travel services
increased sharply in the first quarter of 2024.” From this, two important information is drawn:
First, the total revenue from the three service groups above reached about 189 trillion VND,
equivalent to an increase of 15.3% over the same period last year.
Second, the number of international visitors to Vietnam reached more than 4.6 million, an
increase of 72% compared to the first quarter of 2023 and an increase of 3.2% compared to the
same period in 2019 - the time before the Covid-19 pandemic.
From the information that has been analyzed and linked, in-depth knowledge can be formed:
that is, the Vietnamese tourism industry is entering a period of strong recovery and growth
after the pandemic, with great potential in attracting international visitors and developing
related services. This can suggest strategies for local economic development, infrastructure
investment, tourism image promotion and regional linkage expansion. (NATIONAL STATISTICS
OFFICE, no date)
III. Data evaluation using ,ethods of analysis
3.1. Descriptive data analysis
Descriptive Analytics is one of the four processes of Data Analytics. Specifically, Descriptive
Analytics is the act of collecting and arranging a business's past data in a summary form,
thereby helping the business better understand its past performance to develop the right
strategies for the future.
Some cases of applying descriptive analytics include: annual financial reports, company
personnel statistics, statistics on the number of participants of an event, etc. In which, data is
divided into 2 main types as follows:
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Numerical data (quantitative): specific numerical data such as height, weight, number of
invoices, sales revenue, etc.
Non-numeric data (qualitative): data that reflects the nature and is difficult to determine the
average value such as color, gender, industry, etc. (Top cv, 2023)
3.2. Exploratory data analysis
Exploratory data analysis (EDA) is the process of describing data using statistical and
visualization techniques to bring important aspects of that data into focus for further analysis.
This involves examining a dataset from multiple perspectives, describing and summarizing it
without making any assumptions about its content. EDA is an important step to take before
diving into statistical or machine learning modeling, to ensure that the data is what it claims to
be and that there are no obvious errors. It should be part of data science projects in every
organization. (Pratik, 2025)
3.3. Confirmatory data analysis
Confirmatory factor analysis (CFA) is a complex statistical technique used to verify the factor
structure of a set of observed variables. It allows researchers to test the hypothesis that there is
a relationship between the observed variables and their underlying latent constructs. CFA
differs from Exploratory Factor Analysis (EFA), in that the analysis determines the structure of
the data rather than having a pre-determined structure. (Statistics Solutions, 2025)
3.4. Evaluate data from variety of source
3.5. “Critically evaluate” “ in application”
IV. Analyze and evaluate data using statistical methods
4.1. Qualitative and quantitative data
Criteria Quantitative Analysis Qualitative Analysis
Measurement, hypothesis testing, Deep understanding of behavior,
Objective
relationship determination motivation, emotion or experience
Type of data Numerical data (numbers, Non-digital data (text, audio, images,
used statistics) speech, observation)
Survey using questionnaires with
Collection In-depth interviews, focus groups,
scales, secondary statistics,
method field observations
experiments
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Data Analysis using statistical software: Coding, categorization,
processing SPSS, R, Excel, Stata, ... content/narrative analysis
Numerical results: average,
Results in the form of descriptions,
Output results standard deviation, correlation
themes, quotes, stories
coefficient, ...
Can be generalized to the whole if
Not generalizable, only specific to
Generality the sample is large enough and
each specific context
random
High - less affected by the Lower – depends on the researcher’s
Objectivity
subjectivity of the researcher perspective and interpretation
Understanding customer emotions
Application Consumer behavior research
about the brand through in-depth
examples through survey statistics
interviews
Easy to compare, accurate, Deep understanding of complex
Advantages
reliable, can be tested phenomena, grasping the context
Lack of depth, difficult to grasp Not generalizable, time-consuming to
Limitations
specific context analyze
4.2. Statiscal methods
4.2.1. Descriptive statistics
Descriptive statistics is a method in statistics used to summarize and present important
information about data. It helps us understand the basic characteristics of the data set we are
working with. Descriptive statistics often include calculating parameters such as mean, median,
variance, standard deviation, and other characteristics of the data.(CoderSchool, 2023)
4.2.2. Inforential statistics
Inferential statistics is a branch of statistics that makes the use of various analytical tools to
draw inferences about the population data from sample data. Apart from inferential statistics,
descriptive statistics forms another branch of statistics. Inferential statistics help to draw
conclusions about the population while descriptive statistics summarizes the features of the
data set.(CUEMATH, no date)
4.2.3. Measuring association
The measures of association refer to a wide variety of coefficients (including bivariate
correlation and regression coefficients) that measure the strength and direction of the
relationship between variables; these measures of strength, or association, can be described in
several ways, depending on the analysis.(statisticssolutions, no date)
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4.2.4. The differences in application between Descriptive statistics, Inferential statistics &
Measuring association
Aspect Descriptive Statistics Inferential Statistics Measuring Association
To summarize and To draw conclusions To identify and measure
Main
describe the main about a population relationships between
Purpose
features of a dataset based on a sample variables
Sample (used to Two or more variables
Type of Entire population or
generalize to the (either in a sample or
Data sample
population) population)
Mean, median, mode,
Confidence intervals, Correlation coefficient,
standard deviation,
Key Outputs hypothesis tests, p- odds ratio, relative risk,
frequency, percent,
values, estimations regression coefficients
charts
t-tests, chi-square
Tables, graphs, Pearson/Spearman
tests, ANOVA, z-tests,
Common histograms, measures correlation, Cramér’s V, Phi
regression (for
Tools Used of central tendency coefficient, regression
prediction or
and dispersion models
inference)
Reporting average
Testing whether a new Examining whether there is
income, showing age
Application drug improves health a relationship between
distribution, or
Example outcomes in the exercise frequency and
summarizing test
general population blood pressure
scores
SPSS, R, Stata, Python R, Python, SPSS, SAS, STATA
Statistical Excel, SPSS (basic), R
(scipy.stats, (correlation, regression, chi-
Software (summary functions)
statsmodels) square for association)
Results depend on
Cannot be used to
assumptions May not imply causation
make generalizations
Limitations (normality, even if a strong association
or predictions beyond
independence, etc.), exists
the data
sampling errors
"What could be
inferred" about the "How variables are related"
Focus "What is" in the data
population from the to each other
data
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4.3. Analyze and evaluate qualitative data
4.4. Analyze and evaluate quantitative data
V. conclusion
In conclusion, probability and statistics play a critical role in transforming raw data into
meaningful information and actionable knowledge. Through understanding key concepts such
as descriptive, exploratory, and confirmatory data analysis, and applying both qualitative and
quantitative methods, individuals can uncover patterns, test hypotheses, and make informed
decisions.
The distinction and interplay between descriptive statistics, inferential statistics, and measures
of association highlight the importance of selecting the appropriate analytical approach
depending on the research objectives. Furthermore, the ability to critically evaluate data from
diverse sources enhances the reliability and relevance of the findings.
By mastering statistical thinking and analytical techniques, learners not only develop a strong
foundation for academic research but also gain practical tools applicable in real-world contexts
—ranging from business and economics to healthcare, education, and public policy. Ultimately,
statistics empowers us to make sense of complexity, manage uncertainty, and support
decisions with evidence.
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