chapter 2 Nature of Science

What is Science?
Science is a study of the natural world by systematic observation and experiment. Is the systematic
study of the universe and all it encompasses that is based upon facts, observation, and
Science isn't knowledge; it's the process we use to gain knowledge. We use this to develop all kinds
of new technologies, to improve human living conditions, and save lives. Science is neither dogma
not pseudoscience
Dogma is a set of principles laid out as being unquestionably true.
Pseudoscience, which is a false or fake science or system of beliefs that looks like it's based on
scientific ideas but actually doesn't employ or obey the simplest rules of science itself!
*Scientists use hypotheses, theories, and laws to explain our world.
Hypothesis is a single assertion, a proposed explanation of something based on available
knowledge, for something yet to be explained that is subject to further experimentation.
Explanations of natural phenomena
Theory (Model) is a system of assumptions that generalizes results of well-confirmed
hypotheses in order to apply them to a wide range of circumstances. Explanations of natural
Natural or scientific law (Principle) is a theory that is so well confirmed, refined, and tested,
as to be virtually universally accepted, becomes. Unlike theories they are not explanations of
natural phenomena but rather a generalized description based of multiple observations over
time. They are define by their durability, or ability to stay constant over the time. They maybe
be modified and narrowed, but the description stays constant.
When a hypothesis becomes generally accepted by the science community, it becomes a theory.
A scientific law is a statement that summarizes a collection of observations or results from
experiments. Scientific principles that are viewed as fact become laws
Scientific facts are objective observations that have been repeatedly confirmed by data collected by
multiple scientific investigations. Facts are generally accepted as truth but they are never considered
final proof.
Key principles of science include
1. Consistent methods (including statistics and peer review) help humans be objective
Statistics is the practice of collecting and analyzing data in large quantities. Statistics can tell
you how likely it is that your data is coincidental. If a finding is statistically significant, then it was
unlikely to have occurred due to chance alone.
Peer review a process where scientific experts not connected to the study and experiment in
question check it for scientific accuracy, proper methodology, and relevance before the study is
published in a scientific journal. It is where a paper submitted to be published is looked over and
critiqued by scientists
2. The world makes consistent sense.
If a result can be one way one day and different another day, without any pattern, then you can't
make predictions. All scientific hypotheses must be testable: they must make predictions that we
can check to see whether they're true or not. This is considered to be like a science rule, but it's
really just the assumption that the universe makes sense and can be explained.
3. Certainty is impossible.
Conclusions are never certain because new data could always be found. You can only ever
show that something is highly likely to be true.
4. Relationships can be represented with models and laws.
Model is a way of representing a feature of the world in a simple, understandable way.
Law is a basic statement about the universe based on lots of experiments and observations
Science and technology
Science is the study of the world and how it works through collecting data using the scientific
Technology is the application of that scientific knowledge to create devices that solve problems and
carry out tasks.
Science and technology are inescapably interconnected. They are related not only because
technology is the application of science, but also because technology can be used to do science.
Scientific Research
Scientific research is the systematic investigation of scientific theories and hypotheses.
Scientists use the scientific method, describes the processes by which scientists gain knowledge
about the world.
The first to develop and use the current scientific method was an English philosopher who lived in
the 17th century named Francis Bacon
Is a process that helps construct an accurate depiction of our universe and its processes, in order to
answer whatever questions they may have. This method allows scientists to construct questions
about observed phenomena, construct experiments, and analyze results.
The scientific method It's characterized by six key elements:
Questions, where a scientist proposes the problem that he or she wants to solve
Hypotheses, a potential answer to the question at hand. Sometimes, hypotheses look more
like predictions. Is a possible explanation for the observations made
Experiments, is a procedure carefully done to examine the validity of a hypothesis.
Experiments are ordered investigations that are intended to prove or disprove a hypothesis.
Important data comes from performing an experiment
Observations, is a phenomenon that can be witnessed and recorded. Is a statement of
knowledge gained through the senses or through the use of scientific equipment. Observations
are crucial for collecting data. Observations can be:
Quantitative observations, can be measured, such as number, length, mass, or volume.
Qualitative observations, cannot be measured and area general qualities, such as color,
shape, or texture.
Analyses, Data analysis involves comparing the results of the experiment to the prediction
posed by the hypothesis. Based on the observations he or she made, the scientist has to
determine whether the hypothesis was correct
Conclusions, Is a statement of whether the original hypothesis was supported or refuted by
the observations gathered.
These elements are interrelated steps, so they don't always function in the same order. Other, more
subjective skills like creativity, experience, and intuition also have a place in the scientific method.
Science is characterized by professional competition and develops through the collaboration of
scientists in the worldwide community.
Hypotheses are based on observations and verified through experiments or more observations. Sets
of hypotheses are used to generate scientific laws or theories. Both scientific laws and theories can
be used to make predictions and generate experiments. If the results contrast with the predictions,
the theory or law is modified, and the process is started again.
A scientific investigations contains:
Experimental variable, scientist can manipulate it during the course of the investigation. Is the
element of the hypothesis that is being tested.
Independent /Manipulated Variable: factor or condition in an experiment that is changed on
purpose by you, the scientist; often called the manipulated variable. This goes on the X axis of a
graph (bottom). It Is a variable believed to affect the dependent variable. This is the variable that
you, the researcher, will manipulate to see if it makes the dependent variable change (fosforo y
Dependent / Response Variable: factor or condition in an experiment that changes as a result
of the independent variable; often called the responding variable. This is what you measure and
goes on the Y axis (side). It Is the variable a researcher is interested in. The changes to the
dependent variable are what the researcher is trying to measure with all their fancy techniques.
(Taza de crecimiento bacteriano)
Constant: are variables that must be keep constants. If any other variable, apart from the experimental
variable, are altered, then any observed changes that occurs cannot be attributed to the experimental
Experimental control: a set up without the variable being tested.
Other types of variables
Extraneous variables are defined as any variable other than the independent and dependent
Confounding variable, defined as an interference caused by another variable
Control variables, which are variables that are kept the same in each trial.
Scientific evidence are the results that stem from an investigation with experimental and
control variables.
Moderator variables are variables that increase or decrease the relationship between the
independent and dependent variable.
Types of Science Investigations
Descriptive Investigation: Observational research is a type of research method that records
observations of phenomena. Observational research may be split into naturalistic observation and
participant observation. Involve describing and/or quantifying parts of a natural system. Example –
observing cells under a microscope and diagramming what is seen.
Has a research question, procedures, and conclusion
Used when little is known about the topic
No hypothesis or prediction
Key words: Observe, describe, list, identify
Comparative Investigation: a research method that examines statistical relationships between
variables. Involve collecting data on collecting data on different populations/organisms, under different
conditions (ex. Times of year, locations), to make a comparison.
 Has a research question, possible hypothesis, procedures, and conclusion.
 Can have independent/manipulated and dependent/response variables
 No control / control group
 Key words: Compare/contrast, similarity/difference, categorize
Experimental Investigation: which is a type of research that provides strong evidence for causeand-effect relationships. Involve a process in which a “fair test” is designed in which variables are
actively manipulated, controlled, and measured in an effort to gather evidence to support or refute a
causal relationship. Example- Testing the height of a ramp to determine how far a marble will roll.
 All known variables have been identified
 Has a research question, hypothesis, procedures, control, and conclusion
 Has independent/manipulated and dependent/response variables
 All factors can be held constant except the manipulated
Design Experiments
Experiments are designed so that they support or refute a hypothesis and give results in terms
of measurable, objective data
Experiments have to be relevant to the questions, hypotheses, observations, analyses, and
conclusions of the investigation.
Experiment must be repeatable by other scientists so that it holds up in peer review.
Scientists must incorporate controls to ensure that only one variable is tested at a time
Validity and Reliability: How to Assess the Quality of a Research Study
Reliability measures consistency of scores across time or different contexts. There are several
different types of reliability:
Reliable across time; that is, if the same researcher would get the same results if he or she did
the same study at a different time.
Reliable across samples or across groups of people who are participating in the study.
Validity measures if the results of a given study are accurate, true for different kinds of people and
relevant to the real world.
Internal validity making sure that the cause-effect relationship identified in the study is really
there, and there are no other explanations for the results.
External validity the results you get from the sample of participants in your study are true of
people outside of the experiment as well.
Ecological validity the results have meaning in the real lives of everyday people.
Scientific Experiment: Definition & Examples
A scientific experiment it is a test of a hypothesis. Is an organized and detailed series of steps to
validate or reject a hypothesis.
A hypothesis is an explanation about a phenomenon in the natural world.
The scientific experiment is the third step in the scientific method.
Steps in the scientific method include: an observation made, from which a hypothesis is formed; then
an experiment is completed, from which there is an analysis of the experimental results - to include
supporting or rejecting the hypothesis; and, in the end, it is possible that a new hypothesis is formed.
The first to develop and use the current scientific method was an English philosopher who lived in
the 17th century named Francis Bacon. He, and others after him, disagreed with the scientific
method of the time known as deduction. Rather, he felt the scientific method should use induction which is the process used today for developing a hypothesis. However, deductive methods are still
used to test the hypothesis.
Inductive methods are used to determine a hypothesis, but testing the hypothesis is done using
deductive methods.
Reasoning is finding evidence to support or disprove your conclusions.
Inductive reasoning, drawing conclusions from evidence,
Inductive reasoning is the more common way that scientists conduct experiments. Scientists
have an idea of something to study more in depth. Then they go and collect data through
experiments, observations, or surveys. With all of the data in hand, they analyze it to draw out
Inductive reasoning is about collecting data and seeing what patterns or meaning can be
Deductive reasoning, finding evidence to support or disprove conclusions
Deductive reasoning usually happens when a researcher observes something and believes it
to be a common response. The researcher would then develop a theory or conclusion and
then work to find evidence that supports or dismisses it. The reason this type of reasoning is
not as commonly used as inductive reasoning is the risk of only looking for research that
supports your conclusion. It's all scientific, but it has a higher probability of going awry.
Types of Scientific Experiments
The three main types of scientific experiments are experimental, quasi-experimental and
1. - Experimental, or randomized control.
Can show cause and effect in this type
It is the highest level of scientific experiment known.
There is the greatest amount of control.
As the name also implies, the subjects are randomized to a group.
For randomized controlled experiments, everything is the same for both groups except for the
independent variable. The dependent variable (crecimiento bacteriano) is the main focus of the
experiment; it is what's being examined in the experiment. What's changed in the experiment is
the independent variable (fosforo y Nitrogeno). It's changed in the experimental group only - this is
sometimes called manipulation of the independent variable. The control group does not have any
changes in the independent variable.
At the end of the experiment, the scientist examines the difference between the two groups to see if
there was any effect on the dependent variable. If there is a difference, it is reported as a cause-andeffect relationship. In other words, when the independent variable is manipulated, there is an effect
produced. These types of experiments are very important with medications to see if medications are
beneficial and not harmful. The name associated with medication experiments is Randomized Control
Clinical Trial.
2. - Quasi-experimental.
At the end of this type of experiment the researchers examine the correlations between and among
the variables of interest.
The main focus of the experiment is to observe how the variables respond to one another. Identify the
variables that are to remain as constant as possible, while observing the effect of variation on the other
variables of interest (called explanatory variable).
This is also call natural experiments. A natural experiment involves making a prediction or forming a
hypothesis and then gathering data by observing a system. The variables are not controlled in a natural
3. - Observational experiment,
It is used when there is no way to control variables.
These types of experiments happen outside of the laboratory and may be called a non-experimental
method. Just like the quasi-experimental type of method, there is a great deal of effort made to identify
all variables that may be influential on the variable of interest.
Data collection procedures should be consistent - environmental conditions, timing of data collection,
data-collection instruments and data collection procedures used to gain the data should be the same
for each subject in the experiment.
Formulating the Research Hypothesis and Null Hypothesis
1. - Formulate a research questions. What are you interested in? What are you curious about?
2. – Operationalizing-convert the requearch question in a hypothesis, which is an educated prediction
that provides an explanation for an observed event.
Finding a way to measure or quantify a variable. You have to be able to measure it (to confirm
or reject)
Takes a form of an If-then statement
3. - Develop a null hypothesis, defined as a hypothesis that there is no effect. A prediction that there
will be no effect observed during the study. The reason researchers develop a null hypothesis is to
ensure that their research can be proven false. A null hypothesis is the prediction a researcher hopes
to prove false
Research question
Hypothesis null
'What is the effect of bright light on studying?'
'If we increase the amount of light during studying, then the participant's performance
on test scores will decrease.'
There will be no difference in test scores between the different amounts of light.'
Sampling Techniques In Scientific Investigations
Population, is all the members of a group being studied. A population can be made up of anything:
people, trees, households, cars, bottles of shampoo…whatever it is that you're studying.
Sample, is a small portion that represents the characteristics of the overall population
The assumptions about the population are based on a sample
In order for a sample to be representative of the population, it must be random. The randomness of
sampling is very important. If your sample is not random, it doesn't fairly represent the characteristics
of the population.
Type of sampling
Simple random sampling it is a technique use to select samples avoiding bias. In this technique,
each individual must have an equal opportunity to be selected, and each individual selection is
independent of the others.
Systematic sampling this is when samples are selected at specific, predetermined intervals. This is
often used when simple random sampling would be too time-consuming.
Takes a more ordered approach because samples are selected at specific intervals. As long as the
start point is randomly selected, systematic sampling could be every 100th customer, every 3rd tree
or every 140th bag of dog food at the factory.
Stratified sampling is used to select from different categories within the population. By grouping
individuals into relevant categories (such as age or gender), scientists can randomly select samples
from smaller populations that are more similar to each other.
These categories are called 'strata,' hence the name of this type of selection method. Each category,
or stratum, is considered to be a sub-population from which individuals are sampled.
Cluster sampling is used to divide populations into clusters, which are then randomly sampled.
Instead of sampling individuals, the clusters themselves are randomly selected to represent the
population as a whole.
What Is a System?
System is a general set of parts, steps, or components that are connected to form a more complex
whole. Is a series of parts that come together to form a more complex whole to achieve a particular
goal. Anything that is made up of multiple parts (or steps) and does a particular task or allows
something to happen in a particular way is a system.
The one major characteristic of systems is that they have inputs and outputs.
An input is whatever you put into a system.
An output is whatever comes out of the system. Once the system has completed its process, it outputs
the result.
The Two Types of Physical Systems
A physical system where a quantity or
multiple quantities can enter or leave the
Also called an isolated system. A quantity or
multiple quantities cannot enter or leave the
Systems Analysis: Definition & Example
Systems analysis is a problem-solving method that involves looking at the wider system, breaking
apart the parts, and figuring out how it works in order to achieve a particular goal.
It is often used when creating new systems, learning how to use systems that were created by other
people, making changes to current systems, or solving problems.
Simulations and Models
A simulation is a way of imitating a process or change in the real world to predict what will happen or
explain what did happen and why.
A model is a way of physically representing a system or part of the world and how it works. It involves
both how the system looks and the rules, or laws of physics, that govern it. To run a simulation.
Physical Models: Scale Models & Life-Size Models
A physical model is a constructed copy of an object that represents that object. It can be larger
than the object, smaller, or the same size.
There are two main types of physical model: scale models, and life-size models.
A scale model is a model that isn't the normal size. It's usually smaller, but they can also be larger.
While the size is different, the proportions should be the same as the real thing.
A life-size model is exactly what it sounds like - it's the exact size of the thing it's representing.
Advantages of physical models include trying things out that would be impossible in the real world due
to safety, cost, or practical concerns, and that you can visualize things that are extremely tiny,
happened millions of years ago or that otherwise can't be viewed directly.
Disadvantages include that physical models can be expensive, time-consuming to make, and may
need to be rebuilt if destroyed. Sometimes it is impossible to build a large enough model.
Mathematical Models
A mathematical model are tools we can use to approach real-world situations mathematically. Is a
tool we can use to replicate real-world situations and solve problems or analyze behavior and predict
future behavior in real-world scenarios.
Descriptive and inferential statistic are used to summarize and draw conclusion about data in biological
Mathematical and theoretical biology is a branch of biology which employs theoretical analysis,
mathematical models and abstractions of the living organisms to investigate the principles that govern
the structure, development and behavior of the systems, as opposed to experimental biology which
deals with the conduction of experiments to prove and validate the scientific theories.
Mathematical biology aims at the mathematical representation and modeling of biological processes,
using techniques and tools of applied mathematics and it can be useful in
both theoretical and practical research. Describing systems in a quantitative manner means their
behavior can be better simulated, and hence properties can be predicted that might not be evident to
the experimenter. This requires precise mathematical models.
The field is sometimes called mathematical biology or biomathematics to stress the mathematical
side, or theoretical biology to stress the biological side
Mathematical biology: complex biology process that inspire the creation of multifaceted
mathematical models.
Theoretical biology, consist in the developing of complex mathematical models to explain biology
process. There is no observations or experimental results. Because of the complexity of the living
systems, theoretical biology employs several fields of mathematics, and has contributed to the
development of new techniques.
Theoretical biology focuses more on the development of theoretical principles for biology while
mathematical biology focuses on the use of mathematical tools to study biological systems, even
though the two terms are sometimes interchanged.
Physical biology, play a vital role in the understanding of natural processes. Biophysics study
physical laws and principles that describe and explain patterns in biological processes from the
molecular to the system level.
Types of Mathematical Models
Equations are the most common type of mathematical model.
Pie charts, tables, line graphs, chemical formulas, or diagrams.
Chemical formula
A chemical formula is an expression that specifies the types and numbers of atoms present
in a molecule. It can take on the following form:
(Atom abbreviation)N, where N is the number of the atoms in the molecule.
Using Mathematical Models to Solve Problems
Modeling. Modeling could be said to be showing an example of a scenario.
Mathematical modeling is just a representation of a real-world scenario in formula form. Is the
same - it simply refers to the creation of mathematical formulas to represent a real-world problem in
mathematical terms.
Mathematical models are not perfectly accurate. They are representations of a perfect scenario, but
we all know the real world is not perfect. Just because a shirt looks great on the model, does not
necessarily mean it will look good on me in the real world. To combat this accuracy issue with
modeling, it is important to add as many variables into your model as possible. The more variables
you have accounted for, the more accurate your model will be.
Conceptual Models: Definition & Characteristics
An abstraction occurs when we take something real and represent it in a different, often simpler form.
Substituting something real with something that represents it in a simpler way.
A conceptual model is a way of simplifying a concept or, set of concepts, to make a subject or idea
easier to understand or simulate. It shows how factors are related to each other and outlines the flow
of processes or data.
Is a way of representing a particular concept, or set of concepts, that helps people understand or
simulate the subject of that model. Often drawn as diagrams, conceptual models show relationships
between factors and the flow of data or processes.
The main characteristics or goals of a conceptual model are as follows:
It improves a person's understanding of the subject being modeled.
It communicates details between people who need to know them.
It gives a point of reference for people like designers to come up with specific plans.
It provides a document that can be referred to in the future and used when people work
Conceptual models improve people's understanding of the subject being modeled and communicate
details between people. They also provide a point of reference for design work and useful
documentation for the future. When applied in scientific settings, the first of these characteristics is the
most important The conceptual model should improve our understanding of the subject being modeled.
The main weaknesses of conceptual models are that they are usually simpler than reality and that
they can stretch the truth during oversimplification. However, they are central to how the human mind
works and have led to a lot of scientific advances over the last 200 years.