WEEK 1 ASSIGNMENT: EVALUATING HOMEOSTASIS
Submission Instructions
Please complete your answers to the lab questions on this form. Please complete your answers,
and SAVE the file in a location which you will be able to find again. Then, attach and submit the
completed form to the Week 1 Laboratory dropbox in the Ashford University classroom.
Result Tables
Table 4: Respiratory and Heart Rates per Condition
Participant Condition
Respiratory Rate
(Breaths/Minutes)
Heart Rate (Beats/Minute)
Rest
Post-Exercise
Hypothesis
Post-Lab Questions
1. Is your data quantitative or qualitative? How do you know?
2. What type of graph would you use to represent the data from Table 4?
3. Does the data support your hypothesis from Step 1? Why or why not?
4. What is the independent variable in this experiment? What is the dependent variable?
© eScience Labs, 2013
5. What other variables could be tested in this experimental set up?
6. Use the following template to complete a lab report as described in the introduction.
Table: Lab Report
Lab Report Section
Purpose/Explanation
Title
Abstract
Introduction
Materials and
Methods
Results
Discussion
Conclusion
References
© eScience Labs, 2013
Week 1 Lab Exercise (Introduction)
Introduction
What is science? You have likely taken several classes throughout your career as a
student, and know that it is more than just chapters in a book. Science is a process. It
uses evidence to understand the history of the natural world and how it works. Scientific
knowledge is constantly evolving as we understand more about the natural
world. Furthermore, the constant development of equipment and techniques allows us
to gain an increasingly deeper insight into the natural world, how to protect human
health, and how to prevent disease. Science begins with observations that can be
measured in some way, and often concludes with observations from analyzed data.
Following the scientific method helps to minimize bias when testing a theory, and
enhances the likelihood for developing reliable, repeatable health procedures. It helps
scientists collect and organize information in a useful format so that patterns and data
can be analyzed in a meaningful way. As a scientist, you should use the scientific
method as you conduct the experiments throughout this manual.
Observations
The process of the scientific method begins with an observation. For example, suppose
you observe a plant growing towards a window. This observation could be the first step
in designing an experiment. Remember that observations are used to begin the
scientific method, but they may also be used to help analyze data.
Observations can be quantitative (measurable), or qualitative (immeasurable;
observational). Quantitative observations allow us to record findings as data, and leave
little room for subjective error. Qualitative observations cannot be measured. Instead,
they rely on human sensory perceptions. The nature of these observations makes them
more subjective and susceptible to human error. However, qualitative observations are
still able to provide useful information, as discussed below.
Suppose you have a handful of pennies. You can make quantitative observations that
there are 15 pennies, and each is 1.9 cm in diameter. Both the quantity, and the
diameter, can be precisely measured. You can also make qualitative observations that
they are brown, shiny, or smooth. The color and texture are not numerically measured,
and may vary based on the individual’s perception or background.
Quantitative observations are generally preferred in science because they involve
"hard" data. Because of this, many scientific instruments, such as microscopes and
scales, have been developed to alleviate the need for qualitative observations. Rather
than observing that an object is large, we can now identify specific mass, shapes,
structures, etc.
There are still many situations, as you will encounter throughout this lab manual, in
which qualitative observations are useful. Noticing the color change of a leaf or the
change in smell of a compound, for example, are important observations and can
provide a great deal of practical information.
Developing a Hypothesis
Once an observation has been made, the next step is to develop a hypothesis. A hypothesis
is a statement describing what the scientist thinks will happen in the experiment. A
hypothesis is a proposed explanation for an event based on observation(s). A null
hypothesis is a testable statement that if proven true, means the hypothesis was
incorrect. Both a hypothesis and a null hypothesis statement must be testable, but only one
can be true. Hypotheses are typically written in an if/then format. For example:
Hypothesis: If plants are grown in soil with added nutrients, then they will grow faster
than plants grown without added nutrients.
Null hypothesis: If plants are grown in soil with added nutrients, then they will grow at
the same rate as plants grown in soil without nutrients.
Note: If plants grow quicker when nutrients are added, then the hypothesis is accepted and
the null hypothesis is rejected.
There are often many ways to test a hypothesis. However, three rules must always be
followed for results to be valid.
• The experiment must be replicable.
• Only test one variable at a time.
• Always include a control.
Accuracy vs. Precision
Experiments must be replicable to create valid theories. In other words, an experiment
must provide precise results over multiple trials Precise results are those which have very
similar values (e.g., 85, 86, and 86.5) over multiple trials.
Precise arrows may not hit the bulls-eye, but they all hit the same region on the
target.
By contrast, accurate results are those which demonstrate what you expected to happen
(e.g., you expect the test results of three students tests to be 80%, 67%, and 100%).
Accurate arrows all hit the bulls-eye on a target.
The following example demonstrates the significance of experimental
repeatability. Suppose you conduct an experiment and conclude that ice melts in 30
seconds when placed on a burner, but you do not record your procedure or define the exact
variables included. The conclusion that you draw will not be recognized in the scientific
community because other scientists cannot repeat your experiment and find the same
results.What if another scientist tries to repeat your ice experiment, but does not turn on
the burner; or, uses a larger ice chunk. The results will not be the same, because the
experiment was not repeated using the same exact procedure. In order for results to be
valid, repeated experiments must follow the original experiment exactly. Using this
technique, multiple trials performed in this manner should yield comparable results.
Experiment Features
Variables are defined, measurable components of an experiment. Controlling variables in
an experiment allows the scientist to quantify changes that occur. This allows for focused
results to be measured; and, for refined conclusions to be drawn. There are two types of
variables, independent variables and dependent variables.
Independent variables are variables that scientists select to change within the
experiment. For example, the time of day, amount of substrate, etc. can all be independent
variables. Independent variables are also used by scientists to test
hypotheses. Experiments can only have one independent variable. In this way, scientists
can determine if altering the independent variable is the reason for obtaining a different
result.Scientists would not be able to conclusively determine which change effected the
data if more than one independent variable is changed in an experiment. Independent
variables are always placed on the x-axis of a chart or graph.
Dependent variables are variables that scientists observe in relationship to the
independent variable.Common examples of this are rate of reaction, color change, etc. Any
changes observed in the dependent variable are caused by the changes in the independent
variable. In other words, they depend on the independent variable. There can be more than
one dependent variable in an experiment. Dependent variables are placed on the y-axis of a
chart or graph.
A control is a sample of data collected in an experiment that is not exposed to the
independent variable.The control sample reflects the factors that could influence the
results of the experiment, but do not reflect the planned changes that might result from
manipulating the independent variable. Controls must be identified to eliminate
compounding changes that could influence results. Often, the hardest part of designing an
experiment is determining how to isolate the independent variable and control all other
possible variables. Scientists must be careful not to eliminate or create a factor that could
skew the results.For this reason, taking notes to account for unidentified variables is
important. This might include factors such as temperature, humidity, time of day, or other
environmental conditions that may impact results.
There are two types of controls, positive and negative. Negative controls are data samples
in which you expect no change to occur. They help scientists determine that the
experimental results are due to the independent variable, rather than an unidentified or
unaccounted variable. For example, suppose you need to culture (grow) bacteria and want
to include a negative control. You could create this by streaking a sterile loop across an
agar plate. Sterile loops should not create any microbial growth; therefore, you expect no
change to occur on the agar plate. If no growth occurs, you can assume the equipment used
was sterile.However, if microbial growth does occur, you must assume that the equipment
was contaminated prior to the experiment and must redo the experiment with new
materials.
Alternatively, positive controls are data samples in which you do expect a change. Let’s
return to the growth example, but now you need to create a positive control. To do this, you
now use a sterile loop to streak a plate with a bacterial sample that you know grows well
on agar (such as E. coli). If bacteria grows, you can assume that the bacteria sample and
agar are both suitable for the experiment. However, if bacteria does not grow, you must
assume that the agar or bacteria has been compromised; the agar is inhibiting growth, or
the bacteria in the sample are not viable.
Data
The scientific method also requires data collection. This may reflect what occurred before,
during, or after an experiment. Collected data help reveal experimental results. Data should
include all relevant observations, both quantitative and qualitative.
After results are collected, they can be analyzed. Data analysis often involves a variety of
calculations, conversions, graphs, tables, etc. The most common task a scientist faces is unit
conversion. Units of time are a common increment that must be converted. For example,
suppose half of your data is measured in seconds, but the other half is measured in
minutes. It will be difficult to understand the relationship between the data if the units are
not equivalent. (Sample calculation below).
Significant Digits
When calculating a unit conversion, significant digits must be accounted for. Significant
digits are the digits in a number or answer that describe how precise the value actually
is. Consider the following rules:
Addition and subtraction problems should result in an answer that has the same number of
significant decimal places as the least precise number in the calculation. Multiplication and
division problems should keep the same total number of significant digits as the least
precise number in the calculation. For example:
Addition Problem: 12.689 + 5.2 = 17.889 → round to 18
Multiplication Problem: 28.8 x 54.76 = 1577.088 → round to 1580
Scientific notation is another common method used to report a number. Scientific data is
often very large (e.g., the speed of light) or very small (e.g., the diameter of a cell). Scientific
notation provides an abbreviated expression of a number, so that scientists don’t get
caught up counting a long series of zeroes.
There are three parts to scientific notation: the base, the coefficient and the exponent. Base
10 is almost always used and makes the notation easy to translate. The coefficient is always
a number between 1 and 10, and uses the significant digits of the original number. The
exponent tells us whether the number is greater or less than 1, and can be used to “count”
the number of digits the decimal must be moved to translate the number to regular
notation. A negative exponent tells you to move the decimal to the left, while a positive one
tells you to move it to the right.
For example, the number 5,600,000 can be written in scientific notation as 5.6 x 106. The
coefficient is 5.6, the base is 10, and the exponent is 6. If you multiply 5.6 by 10 six times,
you will arrive at 5,600,000. Note the exponent, 6, is positive because the number is larger
than one. Alternative, the number 0.00045 must be written using a negative exponent. To
write this number in scientific notation, determine the coefficient.Remember that the
coefficient must be between 1 and 10. The significant digits are 4 and 5. Therefore, 4.5 is
the coefficient. To determine the exponent, count how many places you must move the
decimal over to create the original number. Moving to the left, we have 0.45, 0.045, 0.0045,
and finally 0.00045. Since we move the decimal 4 places to the left, the exponent is 4. Written in scientific notation, we have 4.5 x 10-4.
Data Presentation
Although these calculations may feel laborious, a well-calculated presentation can
transform data into a format that scientists can more easily understand and learn
from. Some of the most common methods of data presentation are tables and graphs.
A table is a well-organized summary of collected data. Tables should display any
information relevant to the hypothesis. Always include a clearly stated title, labeled
columns and rows, and measurement units.
A graph is a visual representation of the relationship between the independent and
dependent variable.They are typically created by using data from a table. Graphs are useful
in identifying trends and illustrating findings. When constructing a graph, it is important to
use appropriate, consistent numerical intervals. Titles and axes labels should also reflect
the data table information. There are several different types of graphs, and each type
serves a different purpose. Examples include line graphs (Figure 3) and bar graphs (Figure
4).Line graphs show the relationship between variables using plotted points that are
connected with a line.There must be a direct relationship and dependence between each
point connected. More than one set of data can be presented on a line graph. By
comparison, bar graphs compare results that are independent from each other, as opposed
to a continuous series.
Figure 3: Sample line graph. Plant growth, with and without nutrients, over time.
Figure 4: Sample bar graph. Top speed for Cars A, B, C, and D. Note, since there is no
relationship between each car, each result is independent and a bar graph is
appropriate.
After compiling the data, scientists analyze the data to determine if the experiment
supports or refutes the hypothesis. If the hypothesis is supported, you may want to
consider additional variables that should be examined. If your data does not provide clear
results, you may want to consider running additional trials or revising the procedure to
create a more precise outcome.
Percent Error
One way to analyze data is to calculate percent error. Many experiments perform trials
which calculate known values. When this happens, you can compare experimental results
to known values and calculate percent error. Low percent error (20%) indicates that results may be
inaccurate. The formula for percent error is:
Note that the brackets flanking the numerator indicate “absolute value”. This means that
the number in the equation is always positive.
Suppose your experiment involves gravity. Your experimental results indicate that the
speed of gravity is 10.1 m/s2, but the known value for gravity is 9.8 m/s2. We can calculate
the percent error through the following steps:
The scientific method gives us a great foundation to conduct scientific reasoning. The
more data and observations we are able to make, the more we are able to accurately reason
through the natural phenomena which occur in our daily lives. Scientific reasoning does not
always include a structured lab report, but it always helps society to think through difficult
concepts and determine solutions. For example, scientific reasoning can be used to create a
response to the changing global climate, develop medical solutions to health concerns, or
even learn about subatomic particles and tendencies.
Although the scientific method and scientific reasoning can guide society through critical or
abstract thinking, the scientific industry typically promotes lab reports as a universal
method of data analysis and presentation. In general terms, a lab report is a scientific paper
describing the premise of an experiment, the procedures taken, and the results of the
study. They provide a written record of what took place to help others learn and expedite
future experimental processes. Though most lab reports go unpublished, it is important to
write a report that accurately characterizes the experiment performed. Table 3
summarizes the components of a typical lab report.
Homeostasis and Body Regulation
Homeostasis is one scientific concept which can be studied using the scientific
method. Homeostasisdescribes an organism’s ability to maintain a relatively consistent
internal environment. In fact, one of the key features which defines life is the ability for an
organism to respond to its internal and external environments. Homeostasis is seen in
properties such as temperature, pH, and chemical concentrations such as oxygen, iron, etc.
Mammalian organ systems work very closely together to maintain homeostasis.
Devastating consequences can occur if the body deviates from homeostasis. Consider that
human blood pH must remain at approximately 7.35 - 7.45. A person can become seriously,
or even fatally, ill if the blood chemistry causes the pH to rise above or drop below this
range. Regulating carbon dioxide levels in the blood directly regulates the pH of the
blood. Carbon dioxide will form carbonic acid in the blood; by breathing slower the body
retains carbon dioxide and the pH will decrease (i.e., it becomes more acidic). In contrast,
breathing faster eliminates more carbon dioxide and the pH will increase (i.e., it becomes
more basic).
The urinary system also aids in regulating blood pH by maintaining appropriate body fluid
volume. This is accomplished by regulating the amount of water that is excreted in
urine. While performing this function, the concentrations of various electrolytes (such as
the bicarbonate ion) are also controlled. These ion concentrations affect the acidity of a
fluid solution.
The bicarbonate buffer system is used in the blood to regulate pH. The bicarbonate
buffering system involves carbonic acid and bicarbonate ions. Acids react with the
bicarbonate ions, while bases react with carbonic acid. This reaction is also a method for
shifting carbon dioxide through carbonic acid to hydrogen ions and bicarbonate, as shown:
HCO-3+ H+↔ CO2 + H2O
In this way, the respiratory system and the urinary system work together to maintain the
pH of the blood so that it stays within the essential 7.35 - 7.45 range.
Figure 6: Homeostasis also regulates blood pressure. The baroreceptor reflex is one
method used to accomplish this regulation. This process can be defined in the three
steps outlined above.
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