SCIENCE NOTES
“In Science, what is transiently known as truth is dependent on a collectively reviewed burden of proof that is driven by the power of
imagination and the tools our creativity designs to further the understanding of the reality in which they are based upon."
Introduction to Science
The Basics of Science
 Body of knowledge (accumulated of over thousands of years)
 Process: Study method or organized process of using evidence to learn about the natural world
 Field of study that
o Explores the natural world (empirical)
o Collects and organize information (methodic)
o Looks for patterns/connections (logic / deductive reasoning)
o Proposes explanation that can be evaluated by evidence (imagination / inductive reasoning)
 Goal: Investigation/Observation to understand/explain the natural world in order to make useful prediction
 Always changing (dynamic) through inquiry (raising questions) and peer-review (collaborative evaluation)
 The Importance of Creativity: Designing experiments & explaining observations
 Multidisciplinary science: Integrating multiple fields of study
Science and Ethics
 Science is often used to guide decision making in society and thus society guides science as well
 If a scientist is not careful he/she can go too far, too fast and do harm in an effort for learning more. To set limits one should use
certain guiding lights or criteria:
o Consider consequences to society / nature (Social / Natural cost)
o Economic considerations (Financial Cost)
o Laws (Legal cost)
o Moral principles (Right vs. wrong cost)
 What should not be used as models:
o Authority (What someone in power says)
o Majority (What most people say its true)
o Religion (What faith says is true)
o Precedent (It’s been done before)
 Costs should always be weighed against benefits
 Practicality matters
 All guiding lights should be used in conjunction in case one fails (for example: authority, precedent, and majority sometime
determine laws, but even if laws say it is okay it may still not be RIGHT to do so or ECONOMICAL or good for NATURE.
Science vs. Pseudoscience
 How to tell if something is truly scientific or just sounds like it:
o Science can be tested and is backed up by evidence
o Science can be proven wrong or “not right”
o Science changes with time, it never stays the same (re-examined)
o Scientists accept criticism and contest evidence instead of attacking the critic itself
o Scientists present their information in a fair way; no holding back, no secrets, everything is clearly laid out and explained.
 Science & Philosophy
 Examples of pseudoscience
o Internet scams
o Astrology
o Phrenology
 Most often than not science focus on how/when/what questions
 Most why questions are for religion/philosophy
 Be a critic, a skeptic. Do not accept what others say at face value.
 ASK QUESTIONS!!! ASK FOR PROOF!
Steps of the Scientific Method:
1) Asking Questions6
2) Observation
3) Hypothesis
4) Testing
5) Analysis
6) Conclusion
7) Communication
Steps of the Scientific Method in Detail
Test in blue provides an example of the step being applied
Asking Questions
 Identify a problem
 Discovering a solution always comes after finding a problem (Without a problem there is no need)
 Challenging old / creating new (Established ideas & new discoveries)
 Start from general question and then get more specific (Why is the sky blue? What chemicals are in the sky? What chemical is
responsible for making the sky blue? Does the high concentration of nitrogen in the sky have something to do with it being blue?)
 Can be phrased as a problem statement, instead of a question.
“The effect of ____________ (Independent Variable) on ____________ (Dependent Variable)”
(The effect of nitrogen on the color of the sky)
Observation
 Collecting information (data)
o Qualitative: Description with words (adjectives)
o Quantitative: Description with numbers (quantity; measurements; statistics)
 Understanding the problem before attempting to solve it
 In a way, this is the “beginning” since it can be argued that a problem cannot be identified without observation. But sometimes,
the problem is whether or not observation was made before the problem was generated, one thing is for sure: Once there is a
problem, it is easier to solve it once it is understood
(Research independent variable, dependent variable, possible links between them, other variables that could also affect the dependent variable, etc.)
Hypothesis
 Direct answer to Step 1 (Question)
 Based on inferences (educated guesses about the phenomenon; based on observations)
Note: Remember that inferences are not the same thing as hypothesis. Hypothesis are much more than guesses, they are
explanations
 Proposed scientific explanations for a phenomenon
 Unconfirmed predictions that must be tested (results must either reject it of fail to reject it)
 Even if supported in Science, nothing is “right”. Just… “not wrong” until better evidence says otherwise.
 Important even when they are not supported by data. A good hypothesis leads to further investigation, whether right or wrong
 Steps to creating a good hypothesis:
1) Use the Magic Sentence:
Rephrase question as a problem statement:
The effect of ______________ (IV) on _______________ (DV)
2) Identify Variables (using blanks in the statement)
a. IV – Independent (Manipulated) Variable:
The ONLY thing changed on purpose to study the effect on the dependent variable
b. DV – Dependent (Responding) Variable :
What is measured to see if it is affected by the IV (or what is its response to changes in the IV)
Note: The dependent variable is NOT the one that does not change. It may change between groups and that is the
difference what must be measured. But it is not the one that is DIRECTLY changed.. It is the one that changes because
of the change made to another variable (IV)
Formulate hypothesis
Short and to the point
Do not explain variables just explain the relationship between them
Use if / then statement or one of similar format that establishes a direct one-to-one relationship:
If _______________ (IV) then ____________ (DV).
(If this is done to the independent variable, then this will happen to the dependent variable)
(If children play aggressive video games, then they will be involved in more violence-related incidents in school)
Testing
Correlational Studies (Data Collection & Observation Only)
 Sometimes one cannot perform experiments (against the law, cost too much, not practical, would influence the phenomenon being
studied; for example: smoking & cancer; animals in the wild; the sun; etc)
 Then, the best one can do is methodically collect information to establish a connection between events
 Recording of relationships; there is NO true control over variables
 Results in a measurement of how closely related two variables are (correlation)
 Results in Correlation Data  Conclusion statements say: A is related to B (or connected to B)
 Examples: Observation; Archival research; Surveys
(Survey people who play two different types of games and correlate with their satisfaction or fun after it)
Controlled Experiments (Manipulating Variables)
 CONTROL over variables with only ONE thing changing at time (IV);
 Everything else stays the same (Constants)
 The same thing is measured in the same way across all groups (DV)
 Includes a control group where the IV is not changed or manipulated
 Based on the comparison between groups (Experimental vs. Control)
 Results in EMPERICAL DATA £ conclusion statement can say that: A causes B
 Establishing causation between IV and DV (Because one makes sure only one thing changes, one can say that change is what
caused the outcome observed. For example: One set of people plays a game in an HD 42in flat screen TV, with surround sound,
on the best gaming system, with brand new control, in a nice couch, in the comfort of their home, with no distractions. A
completely different set of people plays a different game in a standard definition 5in screen TV, with no sound, on the oldest
possible system, standing up on rough flooring, inside a bare lab room, with people yelling they are bad at it. Then one cannot
fairly compare the groups and say WHICH factor caused them to have more or less fun than the other. So much changed between
them that there would be no way to say WHICH factor was the one to blame for different levels of fun. Controlled experiments do
the opposite. If one makes similar people play different video games and make sure they play on the same TV, with the same
quality sound and video, on the same room, for the same amount of time, using the same system, same interface, and everything
the same except the game, then and only then one can compare how fun each game is.)
 5 Basic Parts:
o IV – What is changed on purpose to study the effect on the dependent variable
o DV – What is measured to see if it is affected by the independent variable
o Constants – What is held at the same level across groups to make sure it will not affect the results
Note: It is important that every variable is defined in a way that implies how it can be measured (this is called
operationalization of variables)
o Control Group – No special treatment (IV not purposely changed)
Note: The control group IS NOT the group where the DV does not change. There may still be s change, but it will not be
because of a change in the IV because no change in IV was introduced in this group. This is important because one can
expect the same random change to have occurred in the experimental group (if all other conditions between them are the
same, as it should be). This usually indicates that some variable that affects the DV was not properly controlled.
o Experimental Groups – Different level of the change of the same variable (IV)
 Constructing a good experiment
1) Make sure to go through steps 1-3 of scientific method first
2) Once a questions is raised and understand it (Problem and background) and hypothesis is formulated using the method above
(which means variables were identified). One can set up groups:
a. Control Group – Receives no special Treatment (Tx): Independent variable is not change
b. Experimental Groups – Each receives different levels of the treatment (at least one group)
3) Establish how the IV will be changed
4) Establish how the DV will be measured to compare the groups
5) Create a procedure to do so while making sure to keep everything CONSTANT except the IV
6) Summarize it all in a step-by step procedure (A well written procedure minimizes errors)
Note: Good experiments address safety and ethical concerns raised by the protocol.
 Controls & Methods to Increase Validity:
o Constants: Keeping other variables that could affect the DV the same ensures that the change in the DV is because of the
change in the IV (see establishing causation above)
o Control Group: Having a group where the IV is not changed allows for analysis of what the change made in the experimental
group truly caused (One cannot say that what a change in the dependent variable was caused through manipulation of the
independent variable if not comparing a group treated to a group that was not. For example, if one wants to prove earthquakes
move furniture, it is necessary to have furniture that is NOT exposed to an earthquake to compare the earthquake group to).
For another reason why this is important see the note under control group above.
o Sampling: This an important experimental method. When testing a population too big to be measured individual by
individual, scientists use samples that must represent the population
o Representative Assortment to groups: When creating experimental, placebo, or control groups, researchers make sure that
groups are “equivalent”. This ensures that different in results are not because of a pre-existing difference between the groups.
o Repetition: Another important experimental method in which researchers take multiple measurements or repeat the entire
experiment several times. (not the same as replication)
 Using averages instead of single measurements ensures researchers that it was nothing peculiar about that ONE time
when the measure was made that influenced the results.
 Ensures researches that the data is trustworthy (reliable)
o Developing better tools and methods:
 Using better tools allows for more precise data
 Using better methods reduces experimental errors
Using Models (Types: Computer / Physical / Mental / Replacement) & Limitations of Models
The Importance of Creativity: Designing experiments & explaining observations
Multidisciplinary science: Studying the same thing from different angles
(While maintaining other factors constant, randomly sort children into groups and make them play two different violent games to see which one is graded as more fun)
Analysis
 Checking data (checking validity: can this data be trusted and does it to say what it is supposed to say?)
o Is the data reliable? (Consistency): Measurements yield the same number for the same thing (Example: Scale always give the
same weight if the weight itself and the method of weighting does not change. No matter how many times it is used, the
result for a certain weight is always the same)
o Is the data accurate? (Correctness): Measurements yield values that are close to the actual value (Example: The scale give the
weight that actually matches the objects weight)
o Is the data precise enough? (Exactness of measurement): Measurements use tools that can be closer to the smallest
measurement possible (Example: Using a stopwatch, one can be more exact than using a head count or a wall clock to
measure the passage of time)
o Determining Confidence Interval / Error Margin: Steps taken to measure the degree can the data to be trusted or the chances
the chances that it is wrong.
 In statistics, there is a degree to which the data is acceptable as representative or reality (95% confidence interval)
 Measurements are sometimes given with an error margin that depends on the tools used to make the measurement (For
example, 150km +/- 3m)
 Understanding the data (What is the data saying about the Hypothesis?)
o Visualize data: Charts, graphs, tables, maps (See note below)
 Bar graphs: Compare different groups
 Line graph: Change over time (2+ lines for comparing more than one group changing over time)
 Pie chart: % distribution or how pieces of a whole compare to others
 Scatter-Plot: Relationships (Common in correlational studies)
o Compute data: Do math & statistics to see if the results are significant. Sometimes one will find averages are different
between the groups, but considering how many subjects where in each sample, a small difference may have been a simple
“chance event”. To say there is a significant difference, the difference must be large enough to be considered something that
could not have happened solely because of chance in the confidence interval used in the experiment. (For example, if one
compares 2 groups of 100 people to check if listening to music helps test scores and find that on average the music group got
1 extra question right out of 50 when compared to the non-music one, was the difference of 1 point actually significant
enough for one to say that music makes a difference in test scores? That’s what statistics is for!)
o Explain data: Use words to explain results
(Data Check: Check that variables were defined correctly, measured with the best tools possible, and that averages from multiple measurements were used)
(Correlational: Computing the correlation between groups with multivariate analysis for other variables that could also affect the relationship. Design tabling/graphing
to demonstrate the relationship.)
(Experimental: Computing whether there was a significant difference between groups. Design Tabling/Graphing to demonstrate the relationship and perform statistics.)
Note: Look for more information on the website, on how to create good graphs and tables.
Mr. DRY MIX: the Dependent variable is the Responding variable that in a graph is recorded on the Y-axis; the Manipulated variable is the Independent
variable and is graphed on the X-axis
All graphs and tables should be labeled (columns, rows, titles, axis, lines, etc) and legends should be used when necessary.
Conclusion
 Restating what is being researched, what the results said about it, and how that can be explained
 In other words, this step clarifies the meaning of the results.
(Results fail to reject the hypothesis that if children play aggressive video games, then they will be involved in more violence-related incidents in school… )
Communication (Sharing & Peer Review)
 Sharing results with peers
 Presentations at meetings/conferences
 Journals, news, and other media publications (audio/video)
 Internet, phone, texting, emailing, social networking
 Ethical Research Conduct Extends to Communication Process:
o Everything should be reported (even if the data rejected the hypothesis it is important to divulge results). All data helps
enhance scientific knowledge.
o Data should never be tempered with (tell others what actually happened)
 Allows scientists to conduct peer-review:
o Verification of data, procedures, and conclusion
o Criticizing/analyzing the works of others
o Replication and exploration of results (different from repetition within an experimental design)
o Expanding research of others (confirming, refining, or correcting hypotheses)
 Essential to the advancement of science
o Allows scientists to build upon the works of others
o Improves scientific field and humanity as a whole
o Ensures that science is always evolving as a method and body of knowledge
(Scientists discuss conclusions with colleagues, present it at conferences, publish articles in scientific journals, and communicate it to the news)
Theory vs. Hypothesis vs. Law vs. Principles vs. Postulates
Hypothesis
 Before testing
 Proposed explanation; prediction
 Based on inferences
 Needs proof (based on inferences/observations)
 Describe the relationship between 2 variables
 Initial steps of research and very specific
 Design to be refuted
Theory
 After testing
 Working explanation; based on conclusions
 Based on a lot of empirical or at least correlation data
 Has proof (Cumulative body of evidence)
 Describe the phenomena as a whole
 Often the result of years or research combining hypotheses and the study
of several phenomena
 Most powerful explanation available (simplest / best)
 Hard to discard, so often adapted (well-substantiated)
 Not the same as the “theory” as it is used in society
Laws
 Not the same as society laws
 Statement of fact that universally and perpetually explains a specific natural phenomenon
 Concise and specific description of a natural relationship that is constant throughout the universe
 Analytic statement with an empirically determined constant (description based on evidence)
 In other words, it is completely accepted as universally true
 Universally true and invariable concept = Applies in many scenarios (though often not in every single one) & Should not change
(has been observed many times with little or no contradiction)
 Theories often contain sets of laws or a theory can be implied from laws
 Theories do not become laws or vice-versa
 Examples:
o Newton’s Laws of motion
o Kepler’s Laws of planetary motion
o Gravity: “Objects that have mass attract others” (Gravity)
o Thermodynamics (1st Law): “Energy/matter can be changed from one form to another, but it cannot be created or destroyed.
The total amount of energy and matter in the Universe remains constant, merely changing from one form to another.”
(Thermodynamics 1st Law)
o Thermodynamics (2nd Law) “In any closed system, the entropy (disorder) of the system will either remain constant or
increase.” OR “A cyclic transformation whose only final result is to transform heat extracted from a source which is at the
same temperature throughout into work is impossible.” OR “A cyclic transformation whose only final result is to transfer
heat from a body at a given temperature to a body at a higher temperature is impossible.”
o Thermodynamics (3rd Law):
1. It is impossible to reduce any system to absolute zero in a finite series of operations.
2. The entropy of a perfect crystal of an element in its most stable form tends to zero as the temperature approaches
absolute zero.
3. As temperature approaches absolute zero, the entropy of a system approaches a constant
 The Idea of scientific law is problematic because it assumes that it is something is set in stone and ALWAYS true. The very
nature of science is to question things. Even as it tries to understand and predict it all, science accepts the impossibility of ever
reaching a final conclusive solution. Very little is truly universal and never changing. Even concepts like entropy, conservation of
matter/energy, and gravity, and laws of motion do not apply in “every” scientific scenario.
 A few examples of ideas previously seen as universal scientific laws being contradicted:
 In certain places of the universe, matter and energy disappear and/or appear into/from nothingness (black holes for example).
 The effect of gravity is inexplicably negated at a cosmic scale by dark energy which causes the universe to expand at an ever
increasing rate.
 Even though it was postulated as impossible, absolute zero is the actual theoretical end for the Universe.
 Newton’s laws of motion have been expanded, revised, and completely challenged by ideas such as relativity. Objects are no
longer described as falling on straight lines, but as rolling down gravity hills of bent space-time continuum around massive
objects!
 Conclusion: There should be no “scientific law”, just very good theories. However, people use it to describe precepts that seem to
be almost always universally true and very unlikely to change because they are based on theories that have survived testing a
multitude of times in many different situations/applications. They are useful descriptions of the nature of things that is so widely
accepted that they go beyond theory (working explanations). .
Note: In the past, it was much easier to get to this point as skepticism in science was not as common as it is today. If a famous
scientist or a scientist that pioneer a brand new idea came along with a theory, he/she often call the precepts of his theory “Laws”
(as Newton did with his Laws of Motion or Gravity)
Principles
 Dictionary: Rule that must be followed, that is usually followed, or that can be chosen to be followed. Also the inevitable
consequence of something
 In science: a rule of conduct; a fact of nature; a descriptive or fundamental law
 Examples: “The Scientific Method” is a scientific principle, or rule of conduct; “Birds are alive” is another example of a principle
that is a fact of nature, or descriptive; “The speed of light is the ultimate speed limit of unaltered space-time continuum in
universe”, is an example of a fundamental law that if you were to change would change everything we know about physics.
Postulates
 A logical assumption not necessarily proven or demonstrated by evidence but considered as either self-evident or necessarily true
Example: “It is possible to draw a straight line from any point to any other point”; “light comes from a source
Levels of Truth: Change & Durability of Science


There is a reason why stats professors tell us to say results failed to reject hypothesis (as opposed to supported). It's as if in
science, nothing is ever RIGHT. Just "not wrong", for now...until better/more data fails to reject new hypotheses and thus help
create a new theory. One can never say a hypothesis was proven, confirmed, or supported. One can only say that the results
cannot disprove it. A hypothesis will only stand for as long as it is not rejected by data. Science does not prove things, it explains
them as best it can for the time being. It focuses on bringing more clarity by questioning things. Therefore, scientists never accept
things. Scientists say that based on current data the present explanations should not be rejected.
Nothing in science is sacred or forever protected from change. EVERYTHING in science can change.
o Science welcomes criticism: Skepticism is at the core of science
o Scientific knowledge is constantly under scrutiny: RIGHT vs. NOT WRONG
o Debate, replications, logic/critical thinking, argumentation, consideration of alternative data/explanations are constant
o Information sources, evidence, and methods are constantly challenged for accuracy/reliability/validity
o Learning from right/wrong or success/failure
o Result is durable knowledge that withstands the test of time or changes according to new data or interpretations
o Thus scientific knowledge is both robust/durable AND open to change
Example of the Method in Action
1) Asking Question
What causes erosion?  Does deforestation cause erosion?
Ask yourself what will be measured to answer question (Dependent Variable  Erosion)
Ask yourself what will be manipulated to answer question (Independent Variable –> Deforestation)
2) Observation
Study what is erosion and possible causes (wind, deforestation, water, grade or inclination of ground, and other factors that
could explain increasing levels of it). Learn about the problem before trying to solve it. For example, one finds that the trees
prevent erosion through their roots as they keep the soil in place and drain water. Study: erosion and how to measure it
(Dependent variable), deforestation and how to change it (independent variable), the relationship between the two (IV 
DV), other things such as wind/rain/inclination that could affect the erosion other than deforestation (constants), and how
changing the amount of deforestation t different levels can affect erosion (Levels of IV).
3) Hypothesis:
Step 1 (Rephrasing Question as Problem Statement using Magic Sentence):
“The effect of cutting down trees (IV) on erosion (DV)”
Step 2 (Identify variables)
IV – Cutting down trees (Deforestation) & DV – Amount of erosion
Step 3 (Based on background, formulate hypothesis using concise if/then statement that does not explain what the variables
are, but does explain the relationship between them.)
“If more trees are cut down (change on the IV), then there will be more erosion (predicted change in the DP)”
4) Testing
One can simply observe erosion and collect data on where it happens more based on the number of trees, but then there could
be no causal relationship. One could only say deforestation is related to cutting down trees.
Or one can do an experiment:
Identifying the Parts:
IV - # trees cut down
DV – Amount of erosion
CG – No trees cut down EG – Trees cut down
EG2 – More trees cut down
Constants: Inclination or grade of floor; type of soil; type of plants; amount of rain/wind; etc
Description (This is in recipe-like, outline format. But to save space, it is presented as a text)
1) Create lab with fields of the same type of soil in the same gradient (The same amount of initial soil
should be used in all). 3) Plant the same number of trees in all soils (All trees should be of the same type).
4) Count the numbers of trees in each field once enough time has been given for the trees to reach maturity
and cut down extra trees to make sure that each group has the same number of trees. 6) Manipulate the
number of trees per field in the following manner: In the control group, no additional trees will be cut. In
the experimental group 1, 50% of trees will be cut. In the experimental group 2, all trees will be cut. Make
sure habitats are randomly assigned to conditions. 7) Introduce rain/wind through lab environmental control
in the same fashion for all groups. 6) Measure the amount in tons of soil runoff in through collection bins at
the corners of lab fields connected to gravity-sensitive scales. 7) Repeat experiment at least 3 times. 8)
Perform multigroup anova statistical test to test for significant difference between average soil runoff of
groups.
5) Analysis
a. Consider validity, reliability, precision, and accuracy of measurements
Repetition and calibration of tools leads to reliability and accuracy.
Bins will be weights by balances that calculate weight down to cents of a gram (for increased precision)
Make sure groups under same conditions have consistent results, otherwise data is not reliable (and probably not
valid)
b. Graphs, plot, chart, or table results; Explain results; Do statistics to compare groups with math
Results indicate that there are 3 times more runoff in setting with no trees than in the control group and 1.2 times
more runoff if 50% of trees are cut down. (Could use bar graph to show difference. Label axis, include units , title,
and legend as needed).
6) Conclusion (incomplete, this is just a sample of what should be said)
a. Summary statement: Results failed to reject the hypothesis that cutting down trees increases erosion
b. Interpretation of results: The more trees were cut down trees the more likely for rain/wind to carry off top soil
c. Compare: Other researchers found similar results
d. Explanation: Roots of trees help hold soil in place
e. Limitations / Address Limitations: It is possible that some of the runoff did was not properly collected into the bins.
This could have affected the averages of tons of erosion measured. Future research should use a system that catches all
the runoff through grates on the ground bellow the lab assuring that all runoff is accounted for. Furthermore, this
experiment does not use natural habitats, so there is a limit to how much results can be generalizable. Future experiments
could try to compare the same effects in equivalent natural habitats.
f. Application: Farmers and people should refrain from cutting down trees if soil is to be preserved
g. Future research: Should also explore how different types of soil, plants, and varying amounts of wind/rain affect erosion.
7) Share
a.
b.
c.
d.
e.
Results discussed with colleagues through networking
Results are published in journal of agriculture
Presented at geology conference
Appeared in the news on TV and radio
Other scientist replicated and expanded the research…
Theory vs. Hypothesis
Unlike the hypothesis above, a theory would come after many scientists spend time studying erosion and find a lot of
evidence to support many hypotheses about it. A theory of erosion may include many more factors that cause it. For example,
it discusses amount of wind and air, types of soil and plants, gradient of the floor, quantity of plants, etc. This is a working
explanation for erosion based on collected evidence, not a prediction or possible explanation (which would be a hypothesis)
Law:
As part of the working theory of erosion, statements that describe the known phenomena which have been consistently
observed in many circumstances are called the Laws of Erosion. But since this may yet change if new data adds to the
understanding of erosion, it is pointless to call it a Law, as if it is always true and it will never change – no exceptions. Still, if
a lot of research is done and no exceptions, or better explanations are ever found for a very long time… then people cannot
resist calling it a law.