Experiment 1- Introduction and Safety
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. 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. 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.
Concepts to Explore
The Scientific Method
Observations
Variables
Controls
Data Analysis
Calculations
Data Collection
Percent Error
Scientific Reasoning
Writing a Lab Report
Introduction to Science
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. They rely on sensory perceptions. The nature of these observations makes them more subjective and susceptible to human error.
Let’s review this with an example. 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 provide useful data. 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.
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.troduction to Science
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.
There are often many ways to test a hypothesis. However, three rules must always be followed for results tobe valid.
1.The experiment must be replicable.
2. Only test one variable at a time.
3. Always include a control.
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. 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 precise 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 the burner; or, uses a larger ice chunk. The results will not be the same, because the experiment was not repeated using the same procedure. This makes the results invalid, and proves why it is important for an experiment to be replicable.
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. For example, the time of day, amount of substrate, etc. Independent variables are used by scientists to test hypotheses. There can only be one independent variable in each experiment. This is because if a change occurs, scientists need to be able to pinpoint the cause of the change. Independent variables are always placed on the x-axis of a chart or graph.
If plants grow quicker when nutrients are added, then the hypothesis is accepted and the null hypothesis is rejected.
Accurate results all hit the bulls-eye on a target. Precise results may not hit the bulls-eye, but they all hit the same region.
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 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.
- 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 loop streak a plate with a sample that you know grows well on agar (such as E. coli). If the bacteria grow, you can assume that the bacteria sample and agar are both suitable for the experiment. However, if the bacteria do not grow, you must assume that the agar or bacteria has been compromised and you must re-do the experiment with new materials.
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.