8th Grade Lab Science (Cribb & Duane)
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Chapter 1

Introduction:  The Journey, Not The Destination



You are a scientist.  You may not quite believe that statement, but the truth of the matter is that you think and act like a scientist everyday.  In this class, you will learn to recognize the skills you use that identify you as a scientist.  You will practice those skills every day. Whenever you observe something, state an idea, or notice something unique, you are acting like a scientist.  The key is to truly understand what science is.  In other words, you must first answer the question: What is science?

The word science derives from the Latin world scire, which means "to know".  In essence science is a "way of knowing".  In some of your classes, the "way of knowing" involves reading from textbooks and having class discussions with your teacher.   You will conduct those activities in this class as well, but you will also "do stuff".  The "way of knowing" in science primarily involves actively doing things.  These tasks include observing natural phenomena, asking questions about what you see, wondering what makes things the way that they are, and attempting to find answers to those questions.  To succeed in this class, you must become proficient with those skills.

Yet that "way of knowing" is incomplete in itself to be truly scientific.  As a scientist, you will practice the "way of knowing" in an orderly and systematic manner.  That orderly and systematic manner is referred to as the scientific method.  Becoming a skilled practitioner of the scientific method is one of the goals of your 8th grade science course.  An additional goal involves reporting your findings in structured reports, data tables, diagrams, and graphs, and in written prose, but we'll get to that later.   For now, you simply need to understand the basic steps of the scientific method.



Scientists first state a problem based on some observation.  Technically, it may not be a “problem” but some curiosity instead. Observations help gather information about that problem or curiosity.  From those initial observations, scientists formulate a hypothesis.  A hypothesis is not a wild guess about the problem or curiosity – it is an educated guess.  The distinction is important.  A good hypothesis relies upon careful observations and a wealth of background information.  The more thoughtful and thorough you are in formulating a hypothesis, the better or “more valid” your hypothesis will be.  The next step involves the fun part – actively designing and conducting an experiment that tests your hypothesis.  Identifying and controlling your variables are important steps to ensure that your experiment is valid.  Again, we will detail variables and ways to control them later. 

An experiment requires you to collect data.  Sometimes data involves recording detailed observations of what you think is happening.  These descriptions are subjective in that they may differ from one person to the next as we all have different perspectives.  Such descriptive observations are called qualitative data.  Other data involves making measurements and calculating data using formulae.  This data is more objective in that if done properly, should not differ much between individuals.  Measurements and calculations are called quantitative data.  It is important to become proficient with various measurement tools to ensure that your measurements are accurate and precise.  Focusing on the task at hand and paying attention to details help make skilled measurers and competent lab workers.  Also, to become proficient in measuring in science, you must master the metric system of measurements.  You are probably not too familiar with the metric system of measurements since it is not a part of your daily life. It is important to use the metric system in science because not only is it recognized internationally, but it is also the standard on which all science measurements, calculations, and conversions are based.  Regardless of your nervousness, you will attain fluency in the language of metrics.

An important step in recording and collecting data involves organizing your data into diagrams, data tables, and graphs.  You will have much practice in honing these skills and presenting data in the most effective way. 

After analyzing the data, a scientist reaches a conclusion.  Not all concluding statements are valid however.  Concluding statements depend upon the number of trials for the experiment, the control of the variables, and how carefully the experiment was conducted.  If it is a “good” experiment – well designed and carefully conducted, then the conclusion is deemed valid.  A good experiment also opens a box of new questions as new curiosities about ideas for further study as well as a constructive critique of your experimental procedures evolves.

So in summary, the scientific method involves:

v     Stating a problem or curiosity

v     Gathering Information about the problem or curiosity

v     Forming an hypothesis based on observations and background knowledge

v     Conducting experiments to test the hypothesis

v     Recording and analyzing data

v     Stating a conclusion

Sometimes you will have instant success with this.  Sometimes you will have difficulties and need to redo experiments and revise hypothesis.  Sometimes you will find that your initial hypothesis is way off base.  All of these scenarios are all right.  There is no prize for being first, or for being right all the time.  Remember, that science is a “way of knowing” by doing.  Because of this, it is important to do, and then to make new plans (do again) as experiments develop.  To refer to a well-known maxim – science is the Journey, not the destination.  Remember that as you begin your 8th grade science course.  Also, make sure you enjoy the ride!


Cothron, Giess, and Rezba, Students and Research: Pracitcal Strategies for Science Classrooms and Competitions.  Kendall/Hunt Publishing, Dubuque IA, 1993.

Haber-Schaim, Abegg, Dodge, and Walter, Introductory to Physical Science.  Prentice Hall Inc, Englewood, NJ, 1982.

Hurd, Silver, Bacher, and McLaughlin, Prentice Hall Physical Science.  Prentice Hall Inc, Englewood Cliffs, NJ, 1988.


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Chapter 2:

Doing Science - Identifying Variables and Experimental Design Diagrams




Let’s conduct an experiment about something you probably know about – I mean have a good deal of background information.  Imagine you have just made a paper airplane and are curious about what makes it fly.  You folded a paper and threw it.  Then to see what makes it fly, you took the same piece of paper and changed some aspect of the paper airplane (refolded or added weight) and flew it again, observing the action of the plane.  This is a very simple experiment, but in order to understand what you’ve done and draw a valid conclusion, you must analyze this experiment by identifying the variables of the experiment.



Variables are factors that changed in the course of the experiment.  These are either variables that you purposefully changed or manipulated (a refolded wing for example) or are variables that responded to that change (the distance it flew for example).  The variable that you purposefully changed is called the independent variable – the “you changed it variable”.  The responding variable – the “it changed variable”, is called the dependent variable.  It may seem a bit confusing, but there is an easy way of distinguishing the independent variable from the dependent variable. 

The independent variable (IV) is what you manipulate as you set up the experiment.  As you make a paper airplane, you decide how large the wings are, whether it has flaps or not, if you put weight on the nose of the plane.  These modifications are all examples of independent variables.  The dependent variable (DV) is what you measure or observe as a result of these changes.  In other words, it describes what happens once the experiment begins.  The distance the plane flies, the amount of  “hang time”, whether it flies straight or not are all examples of dependent variables.  Stating the relationship between the IV and the DV is the goal for the experiment.  Predicting the relationship between the IV and the DV is what makes a good hypothesis. 

In order to make a valid hypothesis and to draw a valid conclusion, it is important to keep the number of variables that you are testing and measuring small – typically only one or two at a time.  Testing too many variables at once muddies the waters and makes it more difficult to sort through the data in order to state a valid conclusion.  In science (as in life sometimes) it is better to KISS.  KISS stands for Keep It Simple Stupid (no offence intended)!  Applying the KISS maxim to science, you should keep the number of variables to one or two at a time.

In the example of the paper airplane experiment, let’s imagine that you wanted to see what makes the plane fly the furthest distance.  You made a standard design airplane and flew it.  Then you added flaps, changed the wing size and added weight to the nose of the plane and flew it again.  Which of those independent variables (wing size, nose weight, flaps) affected the flight distance the most?  Did the change of wing size or nose weight affect the flight the most?  Did flaps have any influence?  Based on this experiment, you can’t answer that question because this experiment modifies too many independent variables.   That muddies the waters and makes it difficult to draw a conclusion.  This experiment forgot to KISS!



Let’s imagine that you decided to repeat this experiment, but this time only investigating one independent variable – that of wing size.  You were investigating the affect of the size of the wing on the distance that the paper airplane traveled.  You made a standard design plane, flew it and measured the distance traveled.  Then you refolded the wings to make them even bigger, flew it and measured, and then you refolded it and made the wings even bigger.  How many independent variables is this?

The answer is only one – the size of the wing.  However, in this experiment, the wing size is modified more than once.  That means that there are multiple levels to this independent variable.  The levels of IV for this experiment are the standard wing size, the medium wing size, and the big wing size.  The IV for this experiment is the size of the wing.  The levels of the IV are standard, medium, and big wing sizes.  “Standard”, “Medium”, and “Big” are all qualitative descriptions.  It is better to measure the levels of IV to make them quantitative.  In this experiment, you could measure the total surface area of the wings to make the variable quantitative instead of merely describing the size. 



If you modify the wing size for this experiment, you must make sure that all other factors remain the same.  Those factors that remain the same through all levels of the experiment are called constants.  Constants are also referred to as controlled variables, but if that confuses you, ignore that definition, but remember that constants are those factors that do not change throughout all levels and all trials of your experiment.  In the example of the paper airplane experiment, constants include the place of throwing, the paper itself, the overall design of the plane, the atmospheric conditions etc.  A good experiment has an extensive list of constants.  You should be able to identify at least five constants in each experiment.



In science, it is very important that the results of an experiment are not a “fluke”.  Weird, unexplained, and random things always will occur.  Also, no matter how perfect you may have thought that you conducted an experiment, there is always a chance that you botched the experiment.  In other words, you just messed up! Some people call this “Murphy’s Law”.  Others simple call it being human.  Because we are all humans there is always some experimental error.  Besides, it is not an easy thing to control all the extraneous variables – things that you have no control of anyway. At some time in the course of this year, you will all make mistakes in the laboratory.

However, in “doing” science, there is a structure that accounts for experimental error and the reality that as humans we all “mess up” on occasion.  Conducting repeated trials of experiments ensures that experimental error has limited influence.  Conducting an experiment once is not really sufficient.  What if something “weird” happened?  Conducting an experiment twice is a little better than conducting it once, but still insufficient.  Conducting an experiment a hundred times – well now we’re on to something!  This is what is meant by repeated trials.  Every time the experiment is conducted is a repeated trial.  The more repeated trials for an experiment the effects of chance or random errors are reduced.  The more repeated trials, the better the experiment, and the more confident you can be in analyzing results to draw a valid conclusion.

In this class, your lab group may conduct an experiment only once.  However, if you combine all the class data, you may have up to ten trials.  When conducting experiments, you will need to rely on your classmates to increase the number of repeated trials.  You will analyze the data collected by your classmates in addition to your own group, and state conclusions based on as many repeated trials as possible.  

It is important that the results of an experiment are consistent with an additional repeated trial.  However, repeated trials can happen concurrently – that is at the same time.  The number of repeated trials for an experiment can be confused with the number of levels of IV in an experiment.  The number of repeated trials is the number of times that the entire experiment – including all levels of the IV, is conducted.  It is the same number as the number of measurements of the DV are taken for each level. If we return to our paper airplane experiment example, the number of repeated trials is equal to the number of experimenters in this case.  If fifteen students through a standard, medium, and big sized wings, the number of repeated trials is fifteen!



That brings us to the concept of the control group.  Students have had a hard time understanding this concept.  The control is the standard for comparing experimental effects.  It is the basis for comparison.  Typically, it is the group (level of IV) that receives no experimental conditions (variables).  In the example of the paper airplane activity, the control would be the airplane that has the standard size wings.  Controls are also useful in determining whether or not fluke things influenced the experiment.  As a basis for comparison, you should also have a reasonable expectation that some result would occur.  For paper airplanes, you should have a reasonable expectation that the standard size wing would fly.  The flights of the medium and big sized wings are then compared to that of the standard size.

When doctors are testing new medicines, they always use a control group as a standard for comparison.  They administer medicines to people with a particular ailment and compare the results of those patients with a group of patients who have the same ailment but did not receive treatment.  The group that did not receive treatment is the control group.  Sometimes, you will conduct an open-ended experiment that compares variables that does not have a control group.  Regardless, the best experiments include controls.



Identifying the variables and constants of an experiment is the first step to becoming a proficient lab scientist.  The next step involves formatting your variables in an organized way so that the others can easily deduce what the experiment is about.  The format that you will use is called an experimental design diagram.  An experimental design diagram is a simple diagram that summarizes the experiment.  Making a simple diagram that communicates the IV, the DV, the constants, the control and the number of repeated trials is an effective way to summarize the concepts.

To complete an experimental design diagram, begin by drawing a rectangle with a ruler.  The IV is written across the top of the rectangle.  Within the rectangle, divide into labeled columns that represent the different levels of the IV.   The number of repeated trials is indicated in each column.  Also, indicate which level of the IV serves as the control for the experiment.  Below the rectangle record the DV and then list all the constants.  You should list at least 5 constants below the DV.  


Guidelines for Experimental Design Diagrams

Format the experimental process

¨      Begin by drawing a rectangle.

¨      Write the independent variable across the top of the rectangle.

¨      Divide the rectangle into labeled columns to represent the different levels of the independent variable.

¨      Identify your control.

¨      Indicate the number of trials in each column.

¨      Write the dependent variable beneath the rectangle.

¨      List the constants beneath the rectangle.

Above the experimental design diagram you need to write the title for the experiment and state your hypothesis.  A proper title for an experiment is a statement that suggests the relationship between the IV and the DV.  “The Paper Airplane Experiment” is a lousy title in that it doesn’t indicate what either the IV or the DV are.  “The Effect of Wing Size on the Flight Distance of a Paper Airplane” is an adequate title.  To write good titles of experiments, use the following template:  The Effect of___________(IV) on _________(DV).  Just fill in the blanks to ensure that your titles are adequate!  


Guidelines for Writing Scientific Titles

Write a statement that relates the independent variable to the dependent variable. 

For example: The Affect of the ___________(IV) on the __________(DV).

As mentioned earlier, an hypothesis is an educated guess or prediction.  In light of what you now know about variables, we can expand upon that definition.  An hypothesis states what the scientist (you) thinks the effect of the IV will be on the DV.  For example, if wing size is increased, then the paper airplane will fly a greater distance.  Phrasing hypotheses as if… then… statements that predicts the relationship between the IV and DV will ensure that your hypotheses are A-okay! 

Table 2.1 Experimental Design Diagram

Title:  The Effect of Wing Size on the Flight Distance of a Paper Airplane

Hypothesis:  If the wing size is increased, then the paper airplane will fly a greater distance.

IV: Size of the Wings

Standard Wings



15 trials

Medium Wings



15 trials

Big Wings



15 trials

DV: Flight Distance

Constants: Temperature, Size of Paper, Weight of  Paper, Launch Site, Number of flaps  




Below are listed several experimental scenarios.  Read each scenario and identify the IV, DV, constants, control, and repeated trials.  Write a title  and state a hypothesis.  Draw a complete experimental design diagram.  The experimental scenarios described below are not perfect and may have some serious flaws.  The better design detective that you are, the more flaws in each scenario you will uncover.

Scenario 1: Compost and Bean Plants

After studying about recycling, members of John’s science class investigated the effect of various recycled products on plant growth.  John’s lab group compared the effect of different aged grass compost on bean plants.  Because decomposition is necessary for release of nutrients, the group hypothesized that older grass compost would produce taller bean plants.  Three flats of beans (25 plants / flat) were grown for 5 days.  The plants were fertilized as follows: Flat A: 450 g of three-month-old compost, Flat B: 450 g of six-month-old compost, Flat C: 0 g compost.  The plants received the same amount of sunlight and water each day.  At the end of 30 days, the group recorded the height of the plants in cm.

Scenario #2: Metals & Rusting Iron

In his science class, Allen determined the effectiveness of various metals in releasing hydrogen gas from hydrochloric acid.  Several weeks later, Allen read that a utilities company was burying lead next to iron pipes to prevent rusting.  Allen hypothesized that less rusting would occur with more active metals.  He placed the following into separate beakers of water: a) 1 iron nail, b) 1 iron nail wrapped with an aluminum strip, c) 1 iron nail wrapped with a magnesium strip, d) 1 iron nail wrapped with a lead strip.  He used the same amount of water, equal amounts (masses) of the metals, and the same type and size of iron nails.  At the end of 5 days, he rated the amount of rusting as small, moderate, or large by analyzing the color of the water.

Scenario #3: Perfumes and Bees’ Behavior

JoAnna read that certain perfume esters (odor causing chemicals) would agitate bees.  Because perfume formulae are secret, she decided to determine whether unknown ester X was present in four different perfumes by observing the bees’ behavior.  She placed a saucer containing 10 mL of the first perfume 3 m from the hive.  She recorded the time required for the bees to emerge and made observations about the bees’ behaviors.  After a 30 minute recovery period, she tested the second, third, and forth perfumes.  All experiments were conducted on the same day when weather conditions were similar – that is air, temperature, and wind.

Scenario #4: Fossils and Cliff Depth

Susan observed that different kinds and amounts of fossils were present in a cliff behind her house.  She wondered why changes in fossil content occurred from the top to the bottom of the bank.  She marked the bank at five positions: 5, 10, 15, 20, and 25 m from the surface.  She removed one bucket of soil form each of the positions and determined the kind and number of fossils in each sample.

Scenario #5: Aloe vera and Planaria

Jackie read that Aloe vera promoted healing of burned tissue.  She decided to investigate the effect of varying amounts of Aloe vera  on the regeneration of planaria.  Planaria are aquatic flat worms that regenerate body parts when severed.  Jackie bisected the planaria to obtain 10 parts (5 head sections and 5 tail sections) for each experimental group.  She applied concentrations of 0%, 10%, 20%, and 30% Aloe vera to the groups.  Fifteen mL of Aloe vera solutions were applied.  All planaria were maintained in a growth chamber with identical food, temperature and humidity.  On day 15, Jackie observed the regeneration of planaria parts and categorized the development as full, partial, or none.

Scenario # 6: Cartons and Hole Height

Susie wondered if the height of the hole punched into the side of a quart-sized milk carton would affect how far from the container the liquid would spurt when the carton was full of liquid.  She used four identical cartons and punched the same size hole in each.  The hole was placed at a different height on the same size of each container.  The height of the holes varied in increments of 5 cm ranging from 5 cm to 20 cm from the base of the carton.  She put her finger over the holes and filled the carton to a height of 25 cm with water.  When each carton was filled to the proper level, she placed it in the sink and removed her finger.  Susie measured how far from the carton’s base the water had squirted when it hit the bottom of the sink.


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Independent variable (IV): the variable that is purposefully changed by the experimenter.

Dependent variable (DV): the variable that responds.

Constants (C): all factors that remain the same and have a fixed value.

Control: the standard for comparing experimental effects

Repeated trials: the number of experimental repetitions, objects or organisms tested at each level of the independent variable.

Experimental Design Diagram: a diagram that summarizes the independent variable, the dependent variable, constants, control, number of repeated trials, experimental title, and hypothesis.

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Cothron, Giess, and Rezba, Students and Research: Pracitcal Strategies for Science Classrooms and Competitions.  Kendall/Hunt Publishing, Dubuque IA, 1993.

Haber-Schaim, Abegg, Dodge, and Walter, Introductory to Physical Science.  Prentice Hall Inc, Englewood, NJ, 1982.

Hurd, Silver, Bacher, and McLaughlin, Prentice Hall Physical Science.  Prentice Hall Inc, Englewood Cliffs, NJ, 1988.