Biology Assignment

Lab 1

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 not static, but 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. 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 and increase validity when testing a theory. It helps scientists collect and organize information in a useful way 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.

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 testable statement describing what the scientist thinks will happen in the experiment. In other words, it is a proposed explanation for an event based on observation(s). For every hypothesis, a scientist also develops a null hypothesis. 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.

Testing a Hypothesis

There are often many ways to test a hypothesis. However, three rules must always be followed during an experiment for results to be valid.

1. The experiment must be replicable.

1. Only test one variable at a time.

1. Always include a control.

Experiments must be replicable to create valid theories. In other words, the procedure must always be diligently recorded, and 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. 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%). 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.

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 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 develop hypotheses. The “if” part of the hypothesis describes the independent variable and how the scientist will manipulate it. For example, the independent variable in the hypothesis, “If plants are grown in soil with added nutrients, then they will grow faster than plants grown without added nutrients,” is soil with added nutrients. Most experiments can only have one independent variable. However, a hypothesis is usually developed to focus on one variable only. The dependent variable is reflected in the “then” part of the hypothesis. For example, if there is a change in the independent variable, then the dependent variable will change also. Independent variables are always placed on the x-axis of a chart or graph.

Dependent variables are variables which are observed in relationship to the independent variable. 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. Common examples of this are: reaction rate, color change, etc. There can be more than one dependent variable in an experiment. Dependent variables are placed on the y-axis of a graph.

Controls

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.

Collecting and Presenting 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. A common task a scientist faces is unit conversion. Units of time are often displayed in an 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 rules in Table 1:

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 (3 significant digits)

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

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.

Table: A well-organized summary of data collected. Tables should display any information relevant to the hypothesis. Always include a clearly stated title, labeled columns and rows, and measurement units.

Graph: A visual representation of the relationship between the independent and dependent variable. Graphs are useful in identifying trends and illustrating findings. Rules to remember:

1. The independent variable is always graphed on the x-axis (horizontal), with the dependent variable on the y axis (vertical).

1. Use appropriate numerical spacing when plotting the graph, with the lower numbers starting on both the lower and left hand corners.

1. Always use uniform or logarithmic intervals. For example, if you begin by numbering, 0, 10, 20, do not jump to 25 then to 32.

1. Title the graph and both the x and y axes such that they correspond to the data table from which they come. For example, if you titled your table “Heart rate of those who eat vegetables and those who do not eat vegetables”, the graph title should reference this information as well.

1. Determine the most appropriate type of graph. Typically, line and bar graphs are the most common.

Line Graph: Shows 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. Figure 5 uses the data from Table 2.

Bar Graph: Used to compare results that are independent from each other, as opposed to a continuous series. Since the results from our previous example are continuous, they are not appropriate for a bar graph.

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 (<5%) indicates that results are probably accurate, and high 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:

Writing a Lab Report

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.