A 10-Page Module About Experimentation and Data Analysis, Submitted to Sir Ric Buenavie, 7-Armstrong, Mark Bonnin A. Jara.

1. EXPERIMENTATION
WHAT IS EXPERIMENTATION?
An experiment is a procedure carried out to verify, refute, or establish the validity of a
hypothesis. Experiments provide insight into cause-and-effect by demonstrating what outcome
occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale, but
always rely on repeatable procedure and logical analysis of the results. There also exist natural
experimental studies.
A child may carry out basic experiments to understand gravity, while teams of scientists may
take years of systematic investigation to advance the understanding of a phenomenon.
Experiments and other types of hands-on activities are very important to student learning in the
science classroom. Experiments can raise test scores and help a student become more
engaged and interested in the material they are learning, especially when used over time.
Experiments can vary from personal and informal natural comparisons (e.g. tasting a range of
chocolates to find a favorite), to highly controlled (e.g. tests requiring complex apparatus
overseen by many scientists that hope to
discover information about subatomic particles).
Uses of experiments vary considerably between
the natural and human sciences.
Experiments typically include controls, which are
designed to minimize the effects of variables
other than the single independent variable. This
increases the reliability of the results, often
through a comparison between control
measurements and the other measurements.
Scientific controls are a part of the scientific
method. Ideally, all variables in an experiment are controlled (accounted for by the control
measurements) and none are uncontrolled. In such an experiment, if all controls work as
expected, it is possible to conclude that the experiment works as intended, and that results are
due to the effect of the tested variable.
OVERVIEW
In the scientific method, an experiment is an empirical procedure that arbitrates between
competing models or hypotheses. Researchers also use experimentation to test
existing theories or new hypotheses to support or disprove them.
An experiment usually tests a hypothesis, which is an expectation about how a particular
process or phenomenon works. However, an experiment may also aim to answer a "what-if"
question, without a specific expectation about what the experiment reveals, or to confirm prior
results. If an experiment is carefully conducted, the results usually either support or disprove the
hypothesis. According to some Philosophies of science, an experiment can never "prove" a

2. hypothesis, it can only add support. Similarly, an experiment that provides
a counterexample can disprove a theory or hypothesis. An experiment must also control the
possible confounding factors—any factors that would
mar the accuracy or repeatability of the experiment or
the ability to interpret the results. Confounding is
commonly eliminated through scientific controls and/or,
in randomized experiments, through random
assignment.
In engineering and the physical sciences, experiments
are a primary component of the scientific method. They
are used to test theories and hypotheses about how
physical processes work under particular conditions (e.g., whether a particular engineering
process can produce a desired chemical compound). Typically, experiments in these fields
focus on replication of identical procedures in hopes of producing identical results in each
replication. Random assignment is uncommon.
In medicine and the social sciences, the prevalence of experimental research varies widely
across disciplines. When used, however, experiments typically follow the form of the clinical,
where experimental units (usually individual human beings) are randomly assigned to a
treatment or control condition where one or more outcomes are assessed. In contrast to norms
in the physical sciences, the focus is typically on the average treatment effect (the difference in
outcomes between the treatment and control groups) or another test statistic produced by the
experiment. A single study typically does not involve replications of the experiment, but
separate studies may be aggregated through systematic review and meta-analysis.
Of course, these differences between experimental practices in each of the branches of
science have exceptions. For example, agricultural research frequently uses randomized
experiments (e.g., to test the comparative effectiveness of different fertilizers).
Similarly, experimental economics often involves experimental tests of theorized human
behaviors without relying on random assignment of individuals to treatment and control
conditions.
HISTORY
FrancisBacon(1561–1626),anEnglishphilosopher andscientist active in the 17th century,became an
early and influential supporterof experimental science.He disagreed with the method ofanswering
scientific questionsby deductionand described it as follows:"Having first determined the question
accordingtohis will, man then resorts to experience, andbending her to conformitywithhis placets,
leads her aboutlike a captive in a procession."Baconwanteda method that relied on repeatable
observations, orexperiments. Notably, he first ordered the scientific method as we understandit today.

3. Inthe centuries that followed, people whoapplied the scientific method in different areas made
important advancesand discoveries. Forexample, Galileo Galilei (1564-1642) accuratelymeasuredtime
and experimented tomake accuratemeasurements andconclusionsaboutthe speed of a falling body.
AntoineLavoisier (1743-1794),aFrenchchemist,used experiment to describe new areas, suchas
combustionand biochemistry andto develop the theoryof conservationofmass (matter). Louis
Pasteur(1822-1895)usedthescientific method to disprove the prevailing theory ofspontaneous
generation and to develop the germ theory of disease. Because ofthe importance ofcontrolling
potentially confoundingvariables, the useof well-designed laboratory experiments is preferred when
possible.
A considerable amount ofprogress onthe design and
analysis ofexperiments occurredinthe early 20thcentury,
with contributionsfromstatisticians suchas RonaldFisher
(1890-1962),JerzyNeyman(1894-1981),Oscar
Kempthorne (1919-2000),GertrudeMaryCox(1900-1978),
and William Gemmell Cochran(1909-1980),among
others. This early workhas largely been synthesized under
the label of the Rubincausal model, whichformalizes earlier statistical approachesto the analysis of
experiments.
DIFFERENT TYPES OF EXPERIMENTS
Experiments might be categorized according to a number of dimensions, depending upon
professional norms and standards in different fields of study. In some disciplines (e.g.,
Psychology or Political Science), a 'true experiment' is a method of social research in which
there are two kinds of variables. The independent variable is manipulated by the experimenter,
and the dependent variable is measured. The signifying characteristic of a true experiment is
that it randomly allocates the subjects to neutralize experimenter bias, and ensures, over a large
number of iterations of the experiment, that it controls for all confounding factors.
Controlled Experiments – often compares the results obtained from experimental samples
against control samples which are practically identical to the experimental sample except for the
one aspect whose effect is being tested. (Independent Variable)
Examples: drug trial, microbiology, and chemistry.
Natural Experiments – also known as quasi experiments. Natural
experiments rely solely on observations of the variables of the system
under study rather than manipulation of just one or a few variables as
occurs in controlled experiments.
Examples: economics, ecology, geology, paleontology, meteorology,
and astronomy.

4. Field Experiments – are so named to distinguish from laboratory experiments which enforce
scientific control by testing a hypothesis in the artificial and highly controlled setting of a
laboratory often used in social science. Often used in the social sciences, and especially in
economic analyses of education and health interventions.
STEPS TO MAKE AN EXPERIMENT AND A HYPOTHESIS
DIFFERENT SCIENCE PROCESS SKILLS
We use different science process skills to perform or conduct an
experiment, here’s some of them.
Basic Science Process Skills:
1. Observing - using your senses to gather information about an
object or event. It is description of what was actually
perceived. This information is considered qualitative data.
2. Measuring - using standard measures or estimations to
describe specific dimensions of an object or event. This
information is considered quantitative data.

5. 3. Inferring - formulating assumptions or possible explanations based upon observations.
4. Classifying - grouping or ordering objects or events into categories based upon
characteristics or defined criteria.
5. Predicting - guessing the most likely outcome of a future event based upon a pattern of
evidence.
6. Communicating - using words, symbols, or graphics to describe an object, action or event.
7. Quantification – the process of using numbers to express observations.
TEST YOURSELF! FIND WORDS!
U B T W Z J I V C H D C W P W
O V M B X A M Q R L O E S R Z
O S D E L L O R T N O C I O N
U B R F F F K L D U Y Y S C Y
K H S S T M J U U J H Z E E Z
Q D S E H N C N C E Y H H D S
G V C C R T E W U R C V T U R
Q Y I L X V R M O S U M O R G
M J J E J A I T I R N M P E M
C C R G B Y A N H R L U Y X V
S S E C O R P J G G E V H K A
J L W W O N D N Y K L P C F Z
A U C B S Y S I F D N C X U A
F F A I N F E R R I N G T E L
N L X I Z Z K O K T F T F P J
CONDUCT CONTROLLED EXPERIMENT
HYPOTHESIS INFERRING LABORATORY
OBSERVING PROCEDURE PROCESS

6. DATA ANALYSIS
WHAT IS DATA ANALYSIS?
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the
goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Data analysis has multiple facets and approaches, encompassing diverse techniques under a
variety of names, in different business, science, and social science domains.
Data mining is a particular data
analysis technique that focuses on
modeling and knowledge discovery
for predictive rather than purely
descriptive purposes. Business
intelligence covers data analysis
that relies heavily on aggregation,
focusing on business information. In
statistical applications, some people
divide data analysis into descriptive
statistics, exploratory data analysis
(EDA), and confirmatory data
analysis (CDA). EDA focuses on
discovering new features in the data
and CDA on confirming or falsifying existing hypotheses. Predictive analytics focuses on
application of statistical models for predictive forecasting or classification, while text analytics
applies statistical, linguistic, and structural techniques to extract and classify information from
textual sources, a species of unstructured data. All are varieties of data analysis.
Data integration is a precursor to data analysis, and data analysis is closely linked to data
visualization and data dissemination. The term data analysis is sometimes used as a synonym
for data modeling.
THE PROCESS OF DATA ANALYSIS
Analysis refers to breaking a whole into its separate components for individual examination.
Data analysis is a process for obtaining raw data and converting it into information useful for
decision-making by users. Data is collected and analyzed to answer questions, test hypotheses
or disprove theories.
Statistician John Tukey defined data analysis in 1961 as: "Procedures for analyzing data,
techniques for interpreting the results of such procedures, ways of planning the gathering of
data to make its analysis easier, more precise or more accurate, and all the machinery and
results of (mathematical) statistics which apply to analyzing data."
There are several phases that can be distinguished, described below. The phases are iterative,
in that feedback from later phases may result in additional work in earlier phases.

7. DATA REQUIREMENTS
The data necessary as inputs to the analysis are specified based upon the requirements of
those directing the analysis or customers who will use the finished product of the analysis. The
general type of entity upon which the data will be collected is referred to as an experimental unit
(e.g., a person or population of people). Specific variables regarding a population (e.g., age and
income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label
for numbers).
DATA COLLECTION
Data initially obtained must be processed or organized for analysis. For instance, this may
involve placing data into rows and columns in a table format for further analysis, such as within
a spreadsheet or statistical software.
DATA CLEANING
Once processed and organized, the data may be incomplete, contain duplicates, or contain
errors. The need for data cleaning will arise from problems in the way that data is entered and
stored. Data cleaning is the process of preventing and correcting these errors. Common tasks
include record matching, deduplication, and column segmentation. Such data problems can also
be identified through a variety of analytical techniques. For example, with financial information,

8. the totals for particular variables may be compared against separately published numbers
believed to be reliable. Unusual amounts above or below pre-determined thresholds may also
be reviewed. There are several types of data cleaning that depend on the type of data.
Quantitative data methods for outlier detection can be used to get rid of likely incorrectly entered
data. Textual data spellcheckers can be used to lessen the amount of mistyped words, but it is
harder to tell if the words themselves are correct.
EXPLORATORY DATA ANALYSIS
Once the data is cleaned, it can be analyzed. Analysts may apply a variety of techniques
referred to as exploratory data analysis to begin understanding the messages contained in the
data. The process of exploration may result in additional data cleaning or additional requests for
data, so these activities may be iterative in nature. Descriptive statistics such as the average or
median may be generated to help understand the data. Data visualization may also be used to
examine the data in graphical format, to obtain additional insight regarding the messages within
the data.
MODELING AND ALGORITHMS
Mathematical formulas or models called algorithms may be applied to the data to identify
relationships among the variables, such as correlation or causation. In general terms, models
may be developed to evaluate a particular variable in the data based on other variable(s) in the
data, with some residual error depending on model accuracy (i.e., Data = Model + Error).
Inferential statistics includes techniques to measure relationships between particular variables.
For example, regression analysis may be used to model whether a change in advertising
(independent variable X) explains the variation in sales (dependent variable Y). In mathematical
terms, Y (sales) is a function of X (advertising). It may be described as Y = aX + b + error,
where the model is designed such that a and b minimize the error when the model predicts Y for
a given range of values of X. Analysts may attempt to
build models that are descriptive of the data to
simplify analysis and communicate results.
DATA PRODUCT
A data product is a computer application that takes
data inputs and generates outputs, feeding them back
into the environment. It may be based on a model or
algorithm. An example is an application that analyzes
data about customer purchasing history and
recommends other purchases the customer might
enjoy.
COMMUNICATION
Once the data is analyzed, it may be reported in many formats to the users of the analysis to
support their requirements. The users may have feedback, which results in additional analysis.
As such, much of the analytical cycle is iterative.

9. When determining how to communicate the results, the analyst may consider data visualization
techniques to help clearly and efficiently communicate the message to the audience. Data
visualization uses information displays such as tables and charts to help communicate key
messages contained in the data. Tables are helpful to a user who might lookup specific
numbers, while charts (e.g., bar charts or line charts) may help explain the quantitative
messages contained in the data.
CROSSWORD!
Across
3. These are Mathematical Expressions.
4. Refers to breaking a whole into its
separate components.
5. ____ Data Collected (First Step)
6. An outcome of something.
Down
1. ____ Analysis
2. Once the data is analyzed, it may be
reported in many formats to the users of
the analysis to support their requirements.

10. ANSWER KEYS
WORD SEARCH!
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+ + + + + + + + + + O + S R +
O + D E L L O R T N O C I O +
+ B + + + + + + D + + + S C +
+ + S + T + + U + + + + E E +
+ + + E + N C + + + Y + H D +
+ + + + R T E + + R + + T U +
+ + + + + V + M O + + + O R +
+ + + + + + I T I + + + P E +
+ + + + + + A N + R + + Y + +
S S E C O R P + G + E + H + +
+ + + + O + + + + + + P + + +
+ + + B + + + + + + + + X + +
+ + A I N F E R R I N G + E +
+ L + + + + + + + + + + + + +
CROSSWORD!
1. DATA
2.COMMUNICATION
3. ALGORITHMS
4.ANALYSIS
5.RAW
6.PRODUCT