Science & the Scientific Process Method pdf/Science reference...a Scanning Tunneling Electron Microscope. ... • A geologist examining the distribution of fossils in an outcrop

2015

John Schmied and Mike Reid

Microsoft

10/12/2015

Science & the Scientific Process A reference guide

Science & the Scientific Process – a Reference Guide schmied/reid©2015 All rights reserved

Section 1 Introduction to Science Science is many things to many people!

The process of science is used to explore and solve wide ranging issues about our natural world. Scientists study topics from a cure for toenail fungus to issues about our changing climate ! Think of all the problems we are using scientific methods to solve today. For example:

• Finding a cure for Human Immunodeficiency Virus (AIDS)

• Making our homes and businesses more energy efficient

Discovering a cause and cure for Ebola

• Restoring wild Chinook (King) Salmon runs in the Pacific Northwest

• Finding new ways to manage and recycle waste without ruining our environment

• Slowing climate change & ocean acidification by creating alternate energy resources. (Instead of oil & coal).

Science is a tool we use to solve a variety of problems in our society. It is important to know about the basics of science so you have a solid foundation on which to base your science studies. Thus, this section starts with the Seven characteristics of Science, then follows up with What science is – and what it isn’t.

The Seven Characteristics of Science (SHOERAP)

(Many thanks to Professor Roger Olstad, University of Washington for his inspiring thoughts for this section!)

Science is many things, but people who use science agree there are seven characteristics of scientific things. Science is:

• a Structured way of thinking. Science uses a process to solve problems. This process is the Scientific Method! In it scientists figure out how they know what they know and how to show it.

• a Human activity. Science is done by people.


• Observable. Things done in science are things that are able to be observed. After all, the process of science demands evidence. Often scientists need tools to help make observations to get evidence, like the Hubble Space Telescope or a Scanning Tunneling Electron Microscope.

• Experimental. People do science by choosing a question about something in our world. Then people make observations or experiments to try to answer the question.

• Repeatable. If an experiment is done properly anyone ought to be able to repeat the experiment and obtain the same data/results. Every time!

• Assumes the Universe can be ordered. Scientists feel there is an underlying order to how things happen in the universe. They also think there are unifying rules that produce this order, and that these rules are able to be known. Scientists develop better and better ways to order things (like classifying plants, animals, stars, and minerals) in an effort to find a concept or system that will help order the entire universe. Awesome!

• Public and Verifiable. Science is a publicly verified process. All science work is reviewed, and must be verified (discussed and agreed upon as being true based up on the evidence present at the time) by other scientists to be published and used by the world

So what is Science? ref: Dr. Bruce Railsback, Department of Geology, University of

Georgia. http://www.gly.uga.edu/railsback/1122science2.html

http://www.slideshare.net/jschmied/what-is-isnt-science-14224895?from=share_email

Science is the concerted human effort to understand, or to understand better, the history of the natural world and how the natural world works,

….. with observable physical evidence as the basis of that understanding.

Science is done through observation of natural phenomena, and/or through experimentation that tries to simulate natural processes under controlled conditions.

http://www.gly.uga.edu/railsback/1122science2.html

http://www.slideshare.net/jschmied/what-is-isnt-science-14224895?from=share_email


How do scientists do science?

Science studies are generally either Observational or Experimental. Some branches of science mainly use one type, some use some of both. Here are examples of each.

Observational - scientists doing observational science are mainly focusing on collecting data with human senses and tools to answer scientific questions. For example:

• An ecologist observing the territorial behaviors of bluebirds

• A geologist examining the distribution of fossils in an outcrop

• An astrophysicist, photographing distant galaxies to determine the type galaxies and objects within galaxies.

• Lastly, a climatologist sifting data from weather balloons on weather patterns

In each of these examples the scientists are making observations in order to find patterns in natural phenomena.

Experimental Science - On the other hand, scientists doing experiment are using senses and tools during an experiment to answer scientific questions. Examples:

• A chemist observes the rates of one chemical reaction at a variety of temperatures to see what patterns emerge.

• A nuclear physicist records the results of bombarding a particular kind of matter with neutrons in order to see what patterns are present.

• Finally, a Biologist observing the reaction of a particular tissue to various stimulants is likewise experimenting to find patterns.

These scientists are experimenting in order to detect patterns in nature.

The really important things all scientists have in common across the different fields of science is that:


1. Making and recording observations of nature or of simulations of nature, in order to learn more about how nature, in the broadest sense, works. 2. One of the main goals is to show that old ideas (the ideas of scientists a century ago or perhaps just a year ago) are wrong and that, instead, new ideas may better explain nature

Four Things Science Isn't - Another way to look at Science is to see what it isn't.

1. Science Isn't Art - Art is the attempt to express an individual's feelings or ideas about something in a way that others find beautiful, graceful, or at least aesthetically satisfying. Thus art is very individualistic.

On the other hand, science is the attempt to reach demonstrable, replicable, conclusions about the natural world (and social science is the corresponding attempt to reach demonstrable conclusions about the social or human world).

2. Science is not Technology - Science doesn't make things. Scientists generate knowledge. Engineers use scientific knowledge to generate technology.

3. Science isn't Truth and Science isn't certainty - Most scientists seek Truth; they don't know or generate Truth. Scientists use evidence to propose and test theories, knowing that future evidence may cause refinement, revision, or even rejection of today's theories

4. Science isn't a religion or a belief system - Science and belief systems are very different, in what they try to do and in the approaches they use to accomplish their goals. Science seeks to explain the origin, nature, and processes of the physically detectable universe. Science is an activity where the underlying assumptions are tested and retested.

Science Asks Three Basic Questions http://evolution.berkeley.edu/evosite/nature/I3basicquestions.shtml

a. What's there? The astronaut picking up rocks on the moon, the nuclear physicist bombarding atoms, the marine biologist describing a newly discovered species, the paleontologist digging in promising strata, are all seeking to find out, "What's there?"

b. How does it work? A geologist comparing the effects of time on moon rocks to the effects of time on earth rocks; A nuclear physicist observing the behavior of

http://evolution.berkeley.edu/evosite/nature/I3basicquestions.shtml


particles, the marine biologist observing whales swimming, and a paleontologist studying the locomotion of an extinct dinosaur, all ask "How does it work?" c. How did it come to be this way? Each of these scientists tries to reconstruct the histories of their objects of study. Whether these objects are rocks, elementary particles, marine organisms, or fossils, scientists are asking, "How did it come to be this way?"

Section 2 – Types of Investigations and the Scientific Process

The process of doing science is often described as "Organized common sense". However, there is a lot more to it than that. In fact, there is no such thing as "THE one Scientific Method." If you go to science fairs or read scientific journals, you may get the impression that science is nothing more than "question-hypothesis-procedure-data-conclusions." This is not always the way scientists do studies.

Most scientific thinking, whether done while jogging, in the shower, in a lab, or while excavating a fossil, involves continuous observations, questions, multiple hypotheses, and more observations. It seldom "concludes" and never "proves."

Putting all of science in the "Scientific Method" box, with the thought of a white-coated scientist and bubbling flasks, misrepresents much of what scientists spend their time doing. In particular, those who are involved in historical sciences work in a very different way - one in which questioning, investigating, and hypothesizing can occur in any order. http://evolution.berkeley.edu/evosite/nature/IIprocess3.shtml

Please keep these ideas firmly in mind while you work through the rest of this handbook. Science can be done anywhere!

The section of the handbook explores the Scientific Method, knowing that this will be just a starting point for learning about science and the scientific method

As your experience deepens you will learn science isn't always a step by step process, but a whirlpool of ideas, experience, intuition, evidence, and serendipity!

http://evolution.berkeley.edu/evosite/nature/IIprocess3.shtml


Types of investigation - Let’s look at the five types of investigations. Each type of investigation are different types of tool for the scientist’s toolbox. Some types are good for some situations, some for others.

1. Systematic observation is a method of data collection that has two characteristics. It is

an observational technique where investigator(s) gather information without responds, interviews, etc.

a systematic technique in which data are collected according to well defined rules and procedures.

For example, one might study how children use the equipment in a playground; or how deer spaces themselves in a forest. 2. Scientific modeling is the creation of a physical, conceptual, or mathematical representation of a real phenomenon that is difficult to observe directly.

Scientific models explain and predict the behaviour of real objects or systems for many types of science like physics and Earth sciences. (example: Climate modelling, DNA modelling)

http://www.britannica.com/science/physics-science

http://www.britannica.com/science/Earth-sciences


Modeling is a key part of doing modern science. However, models are only approximations of the objects and systems they represent—not exact replicas. Scientists constantly work to improve and refine models.

3. A Simulation imitates of the a real-world process or system operating over time.

The first step in a simulation is to create a model. The model represents the system itself, and the simulation represents the operation of the system over time.

Simulations are used in many ways; for example, when engineering safe products, or testing the limits of a system. Simulations are often used in training & education scenarios.

4. Field studies collect data outside of the lab. Mostly done in the field’ there are many different ways to do field studies. Field studies can take time and cost lots of money, but the amount and diversity of the data collected can be invaluable.

Examples of field studies include studies of the breed habits of elephant seals all the way to studies of the response of insects to a particular chemical.

5. A controlled experiment is a test where the person conducting the test changes only one variable at a time in order to isolate the results.

An experiment where all subjects involved in the experiment are treated exactly the same except for one deviation is an example of a control experiment.

What most people learn in well-rounded Science classes is usually a mix of all five types of investigation, with a healthy emphasis on the Controlled Experiment. However, the controlled experiment is not all there is to science! The other types of investigation are every bit as valid, but focused differently. The Scientific Process - Now that we know the types of scientific investigation, we can move on to a big picture view of the scientific process. A couple facts:

The scientific process is used in all types of investigation.

Conclusions have to be backed up by evidence at all times.

One can start in different parts of the process but one has to cover all parts of the process before an investigation is complete.

All good scientific investigations are reviewed by peers.


The Scientific Process is repeatable. It is the framework for doing science. Scientists can start anywhere in this process to kick off new ideas or rethink old ideas. However, they usually start at the beginning to repeat and recheck previous work.


People who use this process use a lot of creative thinking, work hard, are determined to do their best, and have lots of perseverance, dedication, and common sense. Scientists also find, with experience over time, that there is some incredible flexibility in the process.

The rest of this document is an introduction and review of the scientific process. It also introduces the reader to some standard ways of "showing what you know" while using the scientific process. Please refer to the figure on the previous page as you read ahead.

What’s in a scientist -> Awe, wonder and curiosity about nature Scientists come from many backgrounds, however people interested in science all start out with one thing. That’s a sense of awe, wonder and curiosity about nature

One time, long ago, every scientist was a curious young girl or boy, enthralled by a dragonfly, a frog, the stars, an explosion, or a seriously sick friend being cured. Along the way, the young girl or boy grew up and became an adult. As each got older they retained a sense of wonder about the natural world and then chose to study science as their lives’ work.

Today, as in past centuries, all scientists are dedicated to the study of the natural world. Each scientist is different; some specialize in atomics, ecology, geology or other fields. But their sense of awe and wonder in the natural world frames their vision, drives them forward and inspires their thoughts. This awareness allows scientists to see problems in new ways or see new ways to solve “unsolvable” problems.

As a result, is not a surprise that most scientists have strong desire to keep our environment healthy and sustainable.

Section 3: Stating a Problem - Observations and Inferences

When faced with a new problem, scientists begin by observing and gathering data. This helps describe the problem. Observing and collecting good data is a skill that takes time to learn. Scientific observers recognize or note facts and occurrences in the natural world using tools and senses. (touch, seeing, hearing, smelling, & tasting.)


Scientists are tuned in to receive data and create questions about the natural world. There are many other types of trained observers in the world, for example a Coast Guard Deck Officer conning a ship, or a Police Patrolman, a Doctor or Nurse.

Trained observers see things a bit differently. A trained observer can accurately observe what is happening around them. Scientists are trained to not only see beyond their observations and connect the observations to possible causes.

For example: A scientist, hiking along a Washington stream, observes

the stream no longer has healthy salmon runs. The scientist also observes that

streamside trees & shrubs aren’t healthy.

Later, the scientist observes another stream nearby that has healthy salmon runs and has healthy trees and shrubs. The scientist will naturally be curious and mentally compare the two observations. This is the first step in launching an investigation.

A normal hiker might just be thinking, "What a fine fall day." Or “There doesn’t seem to be any salmon here today.” The scientist, on the other hand, is systematically gathering evidence, observing and comparing data about the two locations.

Taking data - Often what we see and what is actually there are two different things. Thus learning to take good data quantitatively (numbers) and quantitatively (descriptions) help sort things out.

Creating Inferences:

An inference is a reasonable conclusion based upon observations. When comparing observations a scientist notices differences, then goes the next step, which is to take a guess or make inference about why there isn’t a healthy salmon run nor plants on one stream while there are healthy runs and plants on the other.

Inferences are made when you link observations together in your brain and create a preliminary conclusion. Inferences are:

not necessarily accurate! can be strengthened or weakened as an observer gets further data. not seen, felt, heard smelt, or touched. For example one cannot see and

inferences as they are conclusions, not data.


For a thorough review of observations and inferences go to: http://www.slideshare.net/jschmied/observations-vsinferencespart-one-14322159

Example: Let's use the salmon example from the paragraph above to show this. Here are two observations used to make the inferences in the example above.

The forest plants near streams that have poor salmon runs are not healthy.

The forest plants near streams with good salmon runs is healthy.

Now let’s create an inference with these observations. With a little thought we can draw two preliminary conclusions from this data

Inference 1: A stream needs healthy salmon runs to have healthy forest plants. Inference 2: Healthy forest plants help build healthy salmon runs.

Both are reasonable based on the observations.

The process of creating an inference is called Inductive Reasoning, or inferring.

"On any new problem inductive inference is not as simple and certain as deduction, because it involves reaching out into the unknown."

Science, Strong Inference. Proper Scientific Method (The New Baconians) 16 October 1964, Science Magazine, Volume 146, Number 3642 by John R. Platt

Section 4: Developing a Scientific (Testable) Question

Thanks to the: Guide to Scientific Questions http://www.science-house.org/nesdis/gulf/guide.htmlScientific Questions are not like a problem on Rosie, or Oprah, they are entirely different. Scientific questions are designed to investigate the history of the natural world, or how the natural world works.

Scientific questions are usually of three types. Each builds up knowledge of the subject to be tested. This helps the researcher to create a good question.

1. Verification questions are basic data collecting questions. (Is it cold today? What is the temperature/color? Is this how this works?). Each builds up knowledge.

http://www.slideshare.net/jschmied/observations-vsinferencespart-one-14322159

http://www.science-house.org/nesdis/gulf/guide.html

http://www.science-house.org/nesdis/gulf/guide.html


2. Significant questions require an explanation and prior knowledge. (Is it important that this is done first? Do clouds have to be in the sky before it will rain? Is it significant that…. Etc). They increase knowledge of the subject.

3. Experimental questions require explanations and are testable. (If salt is added to the copper sulfate solution, would the solution still boil? or If SPF 45 suntan lotion is put on ultraviolet detecting beads, will they still change color?) They are what researchers use!!

Creating scientific questions is a skill that can be learned, just like sewing. We master this skill when we are able to ask and identify all three types of scientific questions. Let’s take a look at some good and not as good scientific questions.

Guidelines for creating strong scientific questions.

1. A good scientific question is one that can have an answer.

"Why is that a rock?" is not as good as "What are rocks made of?"

2. A good scientific question can be tested by some experiment or measurement that you can do.

In this case "Where does the Sun come from?" is not as good as, "What adaptations do some birds have that allow them to fly??"

3. A good scientific question builds on what you already know.

"Will fertilizer make grass grow greener?" is not as good as, "What is the source of the genetic mutations that cause birth defects?"

4. A good scientific question, when answered, leads to other good questions.

"What is HIV?" does not lead to as many other questions as, "How does the HIV virus cause the immune system to malfunction?"

The questions above ask What and How in a way that focuses in on the specific problem to be studied. Each helps frame a problem in a way that it can be tested.

Some example questions for the salmon situation above might be:


"What goods and services does a healthy forest edge provide to salmon?”

“Does the health of the forest depend on the health of the salmon runs?”

Good questions are well researched. Even the best scientists needs to do research to learn more about their question. There are several reasons for this.

First, research is done to see if the question has been answered already.

Second, if research shows a question, or a similar question, has been attempted by another researcher.

If there is data on how to answer a similar question, the researcher might be able to adapt the procedure and technique used to go further in this area.

If research shows other scientists have been unable to answer the question, then it is important to read their work closely.

Good research is important because the original researchers will report the methods, materials, and experimental design they used. New researchers can try the original set up again to see if there were flaws, or errors, in the procedure. Or they can change the procedure to see if a new procedure will give better results.

Finally, if no one has attempted the problem, a scientist knows there is a fresh open field ahead to work in.

Section 5: Variables & Trials - key elements in setting up an experiment There is one thing researchers have to know after creating a proposed scientific question to study. That is the key elements of the proposed investigation, which are the study subject, the variables and trials to use. Thus, researchers have to identify:

the Study subject, or the object being studied .

the Manipulated or Independent Variable. This is changed to test the hypothesis.

the Responding or Dependent Variable(s). This is measured to get the results

Controlled variables – these are kept the same during the experiment to avoid errors


the Control Trials or Controls. The results of these trials are compared to the experimental trials.

Let’s take a close look at the Variables. These are things that can vary during an experiment. Please refer to the Figure on the next page while reading below.

a. There are two types of variables, Controlled and Uncontrolled variables.

b. There are two different kinds of Controlled Variables.

i. Manipulated variable - one variable changed on purpose to test the hypothesis.

ii. “All others” Controlled variables are controlled, or kept from changing, in an experiment to ensure all trials experience the same conditions. This helps to minimize error. ie. rainfall, sunlight, temperature, humidity, pH, type of food, etc,

c. There are also two different kinds of Uncontrolled Variables.

i. Responding variable - the variable(s) that is/are measured to get the results for your experiment. The responding variable shows the results of changing the manipulated variable. There can be more than one Responding variable. However, in basic science classes, there is usually only one Responding variable.

ii. "All other" Uncontrolled variables - variables that unexpectedly occur during an experiment. Researchers try very hard not to have uncontrolled variables. That’s because uncontrolled variables can cause error & wreck an experiment's results!

"All Others" Uncontrolled variables either: influence an experiment's results and cause error or

don't influence an experiment's results.

Uncontrolled errors can be very costly in terms of time and money! It is best to think an experiment through carefully to identify these ahead of time!


Controlling Variables – There are two ways to control variables!

Create a carefully controlled climate for all trials. (Experiment & Control trials)

Expose the experimental and control trials to the same changing conditions.

Example: if you perform an experiment outdoors, all trials will experience the same changes in the weather. These variables are “controlled” for as all trials are getting the same changes in temperature, humidity, etc.


Example 2 If the experimental and control trials are exposed to different conditions, the experiment's results will be of no use since this is an invalid experiment. Researchers keep conditions the same in both the experimental & control trials For example, in an experiment with mice you would

a. clean and fill the mice's water bottle to the same level every day. b. provide the same amount of food every day, and c. have an auto timer turn the lighting off and on on a regular schedule etc.

Experimental Trials - in an experiment, a researcher creates two sets of trials

where the study subject has identical conditions except that:

the Experimental trials have one variable changed. (the manipulated variable) The Control Trials have no variables changed.

As the experiment progresses, the researchers try to see if the study subjects in the experimental trials react differently compared to the study subjects in the control trials.

Why have Control Trials? Scientists need something to use as a standard to compare the experimental trial results to, so Controls are used. Then scientists can tell that if the results from the control and experimental trials are similar, there was no effect caused by changing the manipulated variable.

The Responding (Measured or Dependent) Variable - The RV is the variable(s) measured in an experiment. Researchers can measure one or more RV in an experiment.

For example, Let’s test to see if different amounts of sunlight will produce bigger beefsteak tomatoes. (Yum!)

a. The amount of sun the tomatoes received is the Manipulated variable (MV). b. The size of the tomatoes is measured is the Responding variable (RV).

ET - In this example the Experimental trial beefsteak tomato plants would receive more sunlight (MV) than the normal amount of sunlight each day.

CT - The Control trial beefsteak tomato plants would receive the normal amount of sunlight for the area.


Over time, the size of the tomatoes from the Control plants are compared to the size of the tomatoes from the Experimental trial plants. The researcher would look at the final data set to see if there was a difference in size between the Experimental Trial tomatoes and the Control tomatoes.

To control all the other variables, in the experiment the researchers would make sure all trials used the same type of soil, the same amount of water, fertilizer, humidity, etc.

Let’s look at another experiment before working on strong questions, predictions and hypothesis.

UV Beads & Sunscreen experiment. A researcher performs an experiment with Ultraviolet radiation detecting beads. His question is: How will applying sunscreen to UV beads affect the beads ability to change color in sunlight?

In the experimental trial there is sunscreen on the UV beads. This is the Manipulated Variable.

In the control trial there is NO sunscreen on the UV beads. The change in color over time of the UV beads is the Responding Variable.

In this experiment, both trials were placed in the sun to see if the UV beads in the experimental trials changed color more compared those in the control trials.

If there was a color difference between the trials, the results would show that sunscreen doesn’t block UV radiation. But if the UV beads in the experimental trial didn’t change color in the sun, then the sunscreen was effective.

Later the researcher might extend this experiment to add more experimental trials with different types of SPF sunscreens applied to the beads. This would let the researcher see if the higher SPF numbered sunscreens were really more effective in blocking in the UV from the sun.

Reliability of Data. Researchers need to be sure their data is repeatable. Experiments are designed to use multiple trials with many samples in each.


In basic science we use the “Rule of Three and Agree”! That means three trials are done and the results of each agree with each other.

Questionable Reliability If there are less than three trials or the trials data doesn’t agree, the reliability of the data is questionable.

Section 7 Creating strong Scientific Questions:

In basic science classes, we ask students to use a specific format to create scientific questions. This format allows students a way to create strong scientific questions for any situation. Here is how this is done.

To create a strong question one needs three factors:

• Study Subject (SS) = the physical object studied.

• Manipulated Variable (MV) = the variable changed to test the hypotheis.

• Responding Variable (RV) = the variable(s) being measured in an experiment.

Basic guidelines for making strong questions: A Question:

• Is phrased to ask for an answer that provides details vs. a yes or no answer.

• tell specifically what is being proposed. Ex: “increasing the amount of fertilizer”

• Contains no Pronouns. Pronouns like "it" are vague, easy to misunderstand. Use proper nouns instead.

Example: A proposed scenario about Greenlander Tulips and Light

Garden fertilizer is applied to 12 Greenlander Tulips in pots

12 other Greenlander Tulips are grown at the same time with no fertilizer.

Both plants are in a similar sized container, given 14 hours of sunlight each day and watered at the same time and amount.

Let's say this experiment went on for a two months and that every week students measured and recorded the height of each Tulip.


Identify the study subject, manipulated variable & responding variable for the experiment.

1. What is the Study Subject?

2. What is the Manipulated Variable?

3. What is the Responding Variable?

Greenlander Tulips garden fertilizer (10:10:10) the height of the Tulips.

Now that you have this information create a question in this format:

How will ...?... Manipulated Variable .... Study Subject.. (either order) affect the.. Responding Variable ?

Note: The Responding Variable should generally be last.

A strong question for the Tulip problem would look something like this:

How will adding fertilizer to Greenlander tulips affect the height of the tulips? This is a strong scientific question because it: a. has an answer that can be obtained using scientific methods.

b. cannot be answered by yes or no.

c. is a specific statement outlining the proposed experiment.

d. contains no pronouns.

Step Three: Develop A Prediction and a Hypothesis

Creating a Prediction.. (If, Then - prediction) Predictions use an

IF, Then format. (If this ___... then ______ will happen... )

Predictions are the next step in developing a hypothesis

In this example we will use this scientific question to create a prediction:

How will covering Ultraviolet detecting beads with suntan lotion affect the beads color change to sunlight?

With this knowledge, it’s possible to come up with this IF - Then prediction


If sunscreen is put on Ultraviolet detecting beads that are placed in the sun,

Then the UV detecting beads with sunscreen will not change color.

This If, Then statement is very good. There is a definite prediction that focuses on sunscreen’s ability to block ultraviolet radiation in UV beads.

Forming a Hypothesis Thanks to: http://www.gpc.edu/~bbrown/psyc1501/methods/hypothesis.htm

The "centerpiece" of research is the hypothesis. The hypothesis reflects the researcher's beliefs about the answers to the question he or she is studying. It is a prediction on steroids!

A Hypothesis: makes a specific prediction about the outcome of an experiment. Is a testable statement about the relationships between or among

variables. (Henslin, 2000/ Shaughnessy, 1994, Cades, 1999)

Is a structured educated guess. tests the prediction's strength.

The simple hypothesis format we use is:

If – Then - Compared to - Because/Therefore

This format starts with a strong prediction – the If - Then

compares to the control trial adds a reason why the hypothesis might be accepted.

Let’s take the UV bead prediction and add to it to create a Hypothesis.

If sunscreen is put on Ultraviolet detecting beads and put in sunlight, (Note the SS & MV are in the IF)

Then the UV detecting beads with sunscreen will not change color

Compared to UV beads without sunscreen.

Because sunscreen prevents UV rays from penetrating to the surface of the UV beads. Therefore the UV beads will not change color.

http://www.gpc.edu/~bbrown/psyc1501/methods/hypothesis.htm


Note: the experimental trial is described in the IF statement. there is a definite prediction of what will happen in the THEN statement. this prediction compares to the Control trial the Because – Therefore statement includes the study subject, the

manipulated & responding variables and a reason why

Truth and Hypothesis: Scientists don't know if the hypothesis will be accepted or not before an experiment is started. Otherwise, doing an experiment is pointless.

Scientists don’t prove hypotheses to be true! Once tested Hypothesis can only be found to be "Accepted" or "Rejected".

A hypothesis cannot be accepted if the hypothesis is not properly tested or

the data is inconclusive. This situation requires further testing and maybe

reworking the procedure to avoid errors.

Faulty Hypothesis a hypothesis can be faulty if the hypothesis does not reflect the actual experiment performed in class.

A twist - Multiple (Alternate) Hypotheses.

Scientists can test more than one hypothesis about the same problem at the same time. This technique goes by a couple names.... The Alternate Hypothesis or Multiple Hypothesis. Why create multiple hypotheses?

a. the use of multiple hypotheses reflects reality. Frequently there are many possible reasons for something occurs the way it does. So testing more than one hypothesis at once is very practical.

b. it forces the researcher to think through the situation and not focus on only one answer before the trial results are analyzed. (this could lead to bias)

c. Investigating multiple hypothesis saves time and money.

For example we can create two hypotheses for this question:

"How does the health of the forest beside streams affect the health of the stream’s salmon runs?"


First hypothesis: If salmon are no longer present in a stream, then the nearby forest will lose nutrients and suffer a loss in health, compared to streams with salmon, because as salmon decompose their nutrients are returned to the forest edge therefore the nearby forest get fertilized.

Alternate hypothesis:

If hunters kill all the bears around a stream, then the nearby forest will become unhealthy compared to streams with bears because bears would excrete wastes (poop) and leave parts of dead salmon therefore fertilizing the streamside forest.

By now you should have a good idea on how to observe, gather information, create a question, make a prediction, and form a Hypothesis. If not, please go back and reread the previous sections, then if you still don't know how to do these things, ask an educated classmate or your instructor for help.

Section 8: Performing the Experiment, or "Testing the Hypothesis"

The entire purpose of doing an experiment is to "test the hypothesis".

Technically, a properly designed hypothesis sets up the design of the experimental trials.

Given a strong scientific question and hypothesis, students can actually visualize the experiment before they start an experiment.

The trials measure a hypothesis's strength by seeing if the things in the BECAUSE phrase actually happen!

Since the experimental design has many elements, it's important to be systematic. I'll list key the experimental elements below, and go over each not covered above in the text following the list.


A Proper Experimental Design:

a. Changes only one variable at a time to test the hypothesis. This is called the manipulated variable in an experiment.

b. Always measures at least one key variable's response. The measured variable is called the responding variable, or independent variable.

c. Controls all other variables possible so all trials experience similar conditions.

Experiments:

• seek to answer a question

• are well researched

• make a prediction

• have a clearly stated hypothesis and test that hypothesis

• changes only one variable at a time (manipulated variable)

• always measures one or more key variables (the responding variable)

• have two types of trials, Control Trials and Experimental Trials.

• have a clear, complete procedure, including all safety procedures

• minimizes all possible errors that could occur (6 general types)

• identifies all materials used and shows/tells how the materials are set up,

• controls as many other variables possible (both controlled variables and

uncontrolled variables)

• documents the results in data tables and displays results on graphs and

figures

• has an understandable analysis of the trials

• conclusion tells if the original hypothesis is accepted or rejected, and uses

evidence from the experiment to support the conclusion. For example Highs,

Lows, Averages, Ranges, Differences etc.


• repeats and recheck results by using 3 or more trials (Reliability).

• shows the Validity of the experiment by doing other trials to demonstrate another way that a change in the Manipulated Variable actually caused a change in the Responding Variable

• communicates the results via a peer review, publishing the results, defends the results and allows the results to be applied.

Writing an experimental procedure

A scientific procedure creates a step-by-step picture of the experiment's methods, materials, safety precautions, and materials.

When writing a procedure, the researcher has to start from the beginning, and carefully put the procedure together step by step.

Procedures include: Background

Safety Precautions A logical sequence of steps to follow

a List of Materials

Procedure writing requires practice. New procedures should be tested and revised before use. The rule of threes applies here... test procedures three times before you are ready.

Peanut Butter and Jelly and Procedure making: Have you ever done the exercise in which students are asked to craft a procedure to make a peanut butter and jelly sandwich? Afterwards another person reads the procedure while the original writer makes an actual sandwich. The results are often hilarious! This is a great way to test out a procedure.... It's the write, test, and revise method.

Once a procedure is written, the old saying, "When all else fails, read the instructions!" applies.

Types of Errors: It's important to write a experimental procedure and do the experiment so all sources of error are minimized. Error is ever present in experiments.


Over the years millions of dollars of experimental data has been rejected due to unidentified errors in procedure and analysis. This make the final data sets unusable.

Researchers are always trying to minimize sources of error in their experiments before, during and after the experiments are completed.

Section 9: So what is error?

https://prezi.com/yt6gg1njmnv3/what-is-error/

Error can be defined to be a mistake in perception, measurement or process.

This definition describes human caused errors. These errors end up in the final results of an experiment.... and the final results are the numbers a researcher analyzes and uses as a basis or their conclusions.

Errors and numbers: Since experimental data is mostly quantitative (data using numbers) researchers use statistics to analyze and evaluate the hypothesis. Statistically speaking, an error is the difference between the true value and the measured value.

There are six general sources of error that can occur in experiments. a. Experimental Design error: This is error is written into the procedure. For example: The procedure overlooks a key element, fails to control variables, skips or reverses steps, doesn't list necessary materials, or isn't clear..etc

b. Operator Error. These are errors made by the people performing the experiment, which are numerous!

c. Observation Error: Observers are human and people make mistakes by reading scales incorrectly, seeing things that don't actually happen the way one thinks is so etc !

d. Recording Error: Human recorders and recording devices make mistakes when recording data. Some of these mistakes are hard to find, especially when a researcher is using tools to record data.


e. Calculation Error: Most everyone who has used a calculator, or added a long string of data is familiar with this source of error! (ex 2.4 + 2.4 = 5.3!)

f. Measuring tool limitation. Occurs when the scale, size or design of a tool is lacking. This means the researcher is not using the correct tool for the job. Say you use a ruler when you really needed a meter stick. Or you use a tool that is cheaper and not as accurate as you need for the job.

As a result, a tool may not measure to the degree of accuracy needed, the measurements may not be as precise as needed, or perhaps the tool has a faulty design and doesn't stay in calibration

Data and Evidence - Data is factual information from investigations that ends up being recorded as numbers or descriptive words to create inferences or hypotheses. Data used to support or reject a hypothesis is called evidence

Qualitative or Quantitative measurements. There are two ways variables can be measured.

Quantative - using numbers (C°, meter/sec etc.). Qualitative - using descriptions (soft, bitter, bigger, sweet).

Section 10: Record and Analyze Results

Observing and recording data properly and accurately will cut down two major sources of error (Observer & recorder error). Both are simple tasks that are easy to mess up.

Analyzing the results requires focus and strong deductive thinking skills. Your data analysis prepares you to create a strong conclusion. The analysis should include:

• A comparison of the experimental trial data to the control trial data

• Search for trends in the trials, data… etc.

• key data from the experiment to use to compare to the control trial's data (examples, high, low, averages and range)

• A survey of the experimental design, data, and analysis for errors


• An assessment of the Reliability of the experiment.

• Data tables, graphs, figures, and or photos to support your final results

Magnificent experiments can lead to spectacular embarrassments if errors go undetected in your analysis. Scientific work is required to be open to public view and one has to make the data available to others upon request! Thus, scientists like to be meticulous when recording and analyzing data. Also scientists thoroughly think through all possible scenarios before creating the final conclusion. This is the next step.

Section 11: Creating a Conclusion

A scientific conclusion is like the conclusion at the end of a mystery novel. It tells who did it, but in the accepted scientific way. Conclusions involve using deductive thinking, a method of thinking that compares the final results to the hypothesis.

All conclusions must clearly

___ Tell the Question being answered

___ State the hypothesis & tell if it was accepted or rejected.

___ Tell why the Hypothesis was accepted or rejected by using supporting evidence from the data analysis. Includes Facts, Reasons, Examples, & Details, or FRED.

___ compare the experimental trial's data to the control trial's data.

So what does key data mean? Key data depends on the experiment!

Experimental trial and control comparisons are always done to show key differences in the data. For example the average increase in height (over time) of the study subject in a growth experiment would be key data to compare, analyze and discuss.

Highs and Lows are used to show the variation in the experimental and control data.

Range is used as an overall indicator of the variation, or as an indicator of the potential error in an experiment. A large range between the High and Low


measurements in a controlled experiment indicates a large variation in results. This might indicate a couple things.

First a large range might indicate errors are present in the experiment (poor design, operations, tools etc.).

Second a large range might indicate that there is no relationship between the manipulated variable and the responding variable. In other words, a change in the MV doesn't cause a change in the RV

On the other hand, a very small range between the experiments High and Low might indicate just the opposite, little error in the experiment...

Students should on the lookout for trends. For example if an increase in pressure in an enclosed container causes an increase in temperature in all trials, this trend would reveal a relationship between temperature and pressure.

Other things that often included in a conclusion.

___ Evaluate & discuss how to improve the data or experiment.

___ Tell all possible sources of error, what caused each and the effect on the data. It is also good to explain how these errors could be minimized or corrected in the future

___ An evaluation of the safety procedures and equipment used.

___ Determine Reliability (Were 3 trials done and do the results agree?

___ Determine Validity, or propose a way to check the experiment's Validity. (Is there another test that can be done to show that a change in the MV actually caused a change in the RV).

Scientists often skip down to the conclusion after they have read the initial summary of your experiment to find out what happened. Afterwards the researchers decide if they want to read the rest of your work.

Strange but true. I know this is kind of like reading the beginning and ending of a book before reading the middle!!! That's because researchers are very busy people and don't like to waste time on work that is poorly thought out, not based upon hard facts, and contains sloppy data.


What is Validity? An experiment with strong validity means its data is of high quality. Strong validity means there is clear evidence that a change in Manipulated Variable actually caused the change in Responding Variable

A way to assure strong validity is to do another, similar, experiment to see if a change in the experiment's manipulated variable actually caused the measured change in the responding variable.

If the results from the second experiment show clearly show similar results; then the validity is strong. If one’s results have strong validity then the question under investigation is being answered with confidence.

After the conclusion, what about the Hypothesis?

a. If the Hypothesis is rejected the scientist starts over, creating a new Prediction or Hypothesis

b. If the Hypothesis is accepted the scientist Repeats and Rechecks the Results of the experiment three times to assure that the results of all experiments agree. If not, its back to testing again. (3X).

Section 12: What’s next - Communicating Results

We started this discussion by saying that good science is many things, SHOERAP.

Three of these things are that science is repeatable, public, and verifiable. Before publication researchers use a Peer Review process to assure their work is fit to print.

Peer Review steps:

a. Make the work available to peer researchers in your field to check. The peer reviewers verify you have used accepted procedures to get your results. In this step you will have to defend your procedures and results to your peers.

b. Publish the information.

c. Defend the Results to the general public. After a scientist gets work published that's not the end of the process. Now the scientist must be ready to defend the conclusions to anyone, scientist or not, anywhere in the world.


d. Apply the information. This is the main point of experimenting, to add to the world's information. This new information can be used to solve problems that will make our world a better place for all living things. Some final thoughts on the terms Hypothesis, Theory, and Law. There's a big difference between scientific Hypothesis, Theory, and Law! Many people use these scientific terms loosely. That's not good form!

i. Hypotheses are proposed explanations for a fairly narrow set of phenomena. These reasoned explanations are not guesses of the wild or educated variety.

When scientists formulate new hypotheses, they are usually based on prior experience, scientific background knowledge, preliminary observations, and logic.

ii. Theories, on the other hand, are broad explanations for a wide range of phenomena.

Theories are concise (i.e., generally don't have a long list of exceptions and special rules), coherent, systematic, predictive, and broadly applicable. In fact, theories often integrate and generalize many hypotheses

iii. Law: In everyday language, a law is a rule that must be abided or something that can be relied upon to occur in a particular situation.

Scientific laws, on the other hand, are less rigid. They may have exceptions, and, like other scientific knowledge, may be modified or rejected based on new evidence and perspectives.

In science, the term law usually refers to a generalization about data and is a compact way of describing what we'd expect to happen in a particular situation.

For further information go to: http://undsci.berkeley.edu/article/intro_01

Ok, with all this knowledge you should be ready to have fun performing experiments!!

http://undsci.berkeley.edu/glossary/glossary_popup.php?word=observe

http://undsci.berkeley.edu/glossary/glossary_popup.php?word=theory

http://undsci.berkeley.edu/glossary/glossary_popup.php?word=law

http://undsci.berkeley.edu/glossary/glossary_popup.php?word=data

http://undsci.berkeley.edu/article/intro_01

Documents

Science & the Scientific Process Method pdf/Science reference...a Scanning Tunneling Electron Microscope. ... • A geologist examining the distribution of fossils in an outcrop