Unit 1: The Nature of Science and Engineering

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1 Unit 1: The Nature of Science and Engineering Key Questions: What is science? How is science different from other disciplines like English or History? What is engineering? What does it mean to say that something is scientific or not scientific? What is scientific knowledge? How is scientific knowledge acquired? What is a hypothesis and how can it be tested? How are variables used in scientific experiments? How can we interpret data from experiments? Goals for this Part: 1. You should be able to describe the differences and similarities between science and engineering and between STEM and other disciplines. 2. You should be able to define scientific theory and scientific law is and to give examples of each. 3. You should be able to describe how scientific knowledge is gained. 4. You should be able to describe the engineering process. 5. You should be able to design an experiment to test a hypothesis. 6. You should be able to identify independent, dependent and controlled variables. 7. You should be able to analyze an experiment and draw conclusions. Part I: What is Science? What Science IS Science is a process by which we try to understand how the natural world works and how it came to be that way. 1. What we can know is limited by our senses and our current tools. 2. Our interpretations of information are influenced by our previous beliefs, no matter how hard we try to be objective. 3. It is impossible to know if we have observed every possible aspect of a phenomenon, have thought of every possible alternative explanation, or controlled for every possible variable. 4. Scientific knowledge changes over time. 5. The development of scientific knowledge follows certain rules as described in CONPTT. 6. Because a theory is supported by multiple lines of evidence, even if one fact or experimental result is disproved, the theory is still supported by the rest.

2 What Science is NOT 1. It cannot solve all kinds of problems and questions. 2. It cannot ignore rules and facts. 3. It does not seek the truth or facts. 4. It does not attempt to prove things. 5. It cannot support supernatural explanations. 6. It does not produce absolute facts; in other words, new information may create changes in what is known. 7. Science is not a process that is always properly used; there is not just one scientific method. 8. Science is not a process free from values, opinions, or bias. 9. It is not a process in which knowledge is based on faith or belief. 10. Scientific theories are not tentative ideas or hunches. 11. Science is not a process in which one solution is as good as another; it is not simply a matter of opinion. Vocabulary: Science: Science involves the study of natural phenomena (events) which can be observed, measured, and tested by scientific methods; using the senses to observe (directly and/or indirectly) and evaluate. Is it science? Ask yourself if the CONPTT criteria are met: Consistent The observations are repeatable and give reasonably consistent results Observable The phenomena can be observed directly or indirectly using the senses Natural Explanations must not involve supernatural or untestable ideas Predictable Explanations can be used to predict future events and outcomes Testable The cause of an event must be testable through controlled experimentation Tentative Scientific explanations can be modified or even overturned as new observations and results are obtained Nonscience A nonscience is a set of beliefs that does not meet the CONPTT criteria for science. Examples of nonscience include religious beliefs, philosophical beliefs, and personal opinions. Pseudoscience Pseudoscience (false science) is a nonscience that is presented and portrayed as scientific. Two examples of pseudoscience are astrology and creation

3 science. Scientific theory: A theory is a testable description of a set of related observations or events based upon proven hypotheses and verified multiple times by detached groups of researchers. Scientific theories are supported by many experiments and are also useful to explain a wide range of observations and to make predictions. Most scientific theories are supported by so much evidence that they are unlikely to change. Note that the term theory is very frequently used incorrectly in books, TV shows, movies, and in the news. Be on the lookout for these incidents! Examples of scientific theories: Plate Tectonics, Theory of Relativity, Evolution, Cell Theory Scientific law: A scientific law describes the behavior of something in nature under certain conditions. It is used to make predictions about behavior. Scientific theories often include laws. Examples of scientific laws: Law of Gravity, Newton s 3 Laws of Motion, Kepler s 3 Laws of Planetary Motion, Special Relativity, General Relativity Part II: What about Engineering? Engineering is applied science. In other words, engineers use scientific knowledge and principles to solve problems and to create solutions. There are as many different types of engineers as there are types of science! Following is an incomplete list of some branches of engineering: environmental engineering biomedical engineering bioengineering genetic engineering agricultural engineering mechanical engineering electrical engineering civil engineering nuclear engineering robotics engineering computer engineering chemical engineering geological engineering aerospace engineering automotive engineering Part III: Science and Engineering Practices The OLD Scientific Method: The Steps of the Scientific Method The scientific method is often taught to children as if there is ONE scientific method that has a step-by-step procedure that is done in a particular order. In fact there is no one scientific method. Although some scientific discoveries may be made using a step-by-step scientific method, others are the result of a looping procedure, where the scientist must repeat or redo parts of his or her research (see below), while still others (like Einstein s theories) are discovered through pure mental reasoning (thought experiments).

4 In fact, real science is rather messy! In reality, a researcher constantly evaluates what he or she is doing and makes adjustments, backtracks, or changes directions. Real science involves a lot of thinking on your feet, creativity, and risk-taking. Scientists and Engineers Engage in Particular Practices The newest science and engineering standards recognize that practicing scientists and engineers, including student scientists, engage in eight particular practices to create new understandings of how the universe is constructed and how it works, and to solve problems and create new innovations. These practices are not done in a particular order, and in fact may be repeated in different loops during the investigation or project. The following graphic illustrates the science and engineering practices as defined by the Next Generation Science Standards. As you can see, doing science includes using communication skills like speaking, reading, and writing, and using mathematics. Most science and engineering is performed collaboratively, with many people contributing to the final discovery or product.

5 The Engineering Design Process One of the practices includes Designing Solutions, which is the main goal of engineering. An engineer starts with a need or problem and goes through a series of processes before a final design is produced. These steps are very similar to the practices illustrated on the previous page. Reverse Engineering Reverse engineering involves starting with a finished product and taking it apart in order to figure out how it works. The purpose is to understand the product s structure, function, and operation. Part IV: Designing Experiments Why do an experiment? An experiment is done to test a hypothesis, which is a predicted answer to a scientific question. An experiment can be done to compare one group to another group or to test

6 the effects of one thing on another. A scientific experiment is not done to demonstrate something that is already known or to see if something can be done. What makes a good research question? After completing background research, a scientist needs to come up with a testable question that he or she wants to investigate. A testable question is one that can be investigated through experimentation. Questions that are NOT testable are those that are based on opinion or preference, moral values, the supernatural, or which cannot be measured. For example, which of the following questions are testable questions? Why or why not? Testable: Yes or No? 1. What do ants eat? 2. Does the movement of planets control our personalities? 3. Are men smarter than women? 4. Does God exist? 5. Are lemurs more closely related to apes or monkeys? 6. Which peanut butter tastes better? 7. What is our purpose on Earth? 8. Do ghosts control the weather? 9. How old is the Earth? 10. Did whales evolve from land mammals? 11. Is this stream healthy? 12. What is the relationship between deer and wolves? 13. Which is the best shade of blue for a sweater? 14. Do people share a common ancestor with gorillas? The following are testable questions: 2, 3, 5, 10, 11, 12, 14, although most of them need to be reworded so that the variables are measurable. For example, #2 would be clearer if it described the particular planetary movements and had a more specific definition of personalities. #3 needs to define what smarter means as there are many different ways to be smart. What is a hypothesis? A hypothesis is a prediction of the outcome of an experiment. It is the predicted answer to the testable question (not a guess or an educated guess ) and should be based on knowledge of scientific information. This knowledge usually comes from the background research on the topic under consideration. In other words, the scientist must find out what is already known about the science topic and use this information to support his or her hypothesis.

7 When you conduct a science experiment, you are testing the hypothesis. In other words, you are seeing if your predicted answer is supported by your results. A hypothesis is usually written in the following format: If then because For example: If sunburns cause skin cancer, then people who have had a lot of sunburns will be more likely to get skin cancer. This is because ultraviolet radiation damages the DNA in the nucleus of cells. Damage to DNA may affect the cell division part of the cell cycle. or If the ph of the soil is changed, then the pea plants grown in soil with a ph between 6 and 7 will grow taller than the pea plants grown in soil with a ph less than 6 or greater than 7. This is because when the ph is between 6 and 7, the bacteria that grow on the roots of the pea plants are able to fix nitrogen for the plant at the highest rate. What are variables? A variable is a factor in an experiment that can change, or vary. There are three main types of variables, the independent variable, the dependent variable, and the controlled variables. The independent variable and dependent variable should be included in both the research question and the hypothesis. Independent variable: The independent variable is the one that the researcher is testing. This is the one that they change or manipulate. Usually, there is just one independent variable. Dependent variable: The dependent variable is a factor that is measured to check the effect of the independent variable. There may be one or more dependent variables. Controlled variables: Controlled variables are factors that the researcher wants to keep the same or control during the experiment. They are kept the same so that they do not affect the dependent variable. (Note: Do not confuse controlled variables with a control group more about this later!) For example, if a student was conducting a science fair project on the effects of Vitamin C on the growth of radish sprouts, the testable question, hypothesis, and variables might be as follows: Research Question:

8 What are the effects of Vitamin C on the number of leaves of radish (Raphanus sativus) sprouts? Hypothesis: If Vitamin C is given to Raphanus sativus sprouts, then they will have more leaves than R. sativus sprouts that are not given Vitamin C. This is because. Variables: Independent Variable: Amount of Vitamin C Dependent Variable: Number of leaves Controlled Variables: The sprouts will be given the same amount of water. The sprouts will be planted in the same type of soil. The sprouts will be planted in the same size containers. Three seeds will be planted in each pot. After sprouting, only one sprout will be left to grow in each pot. The sprouts will be kept at the same temperature throughout the experiment. The sprouts will receive the same amount of natural light. The sprouts will have the same angle of natural light. The sprouts will be watered at the same time each day. The same ruler will be used to measure each of the sprouts. And so on. Experimental Design The purpose of designing an experiment is to test the hypothesis. Often, an experiment is designed to compare two or more groups. Example 1: I might want to compare the speed of a snowboarder with a waxed board to the speed of a snowboarder without wax on her board. Group 1 includes the trials with the waxed board and Group 2 includes the trials for the unwaxed board. Example 2: I want to know if males or females have faster reflexes. Group 1 is one gender and Group 2 is the other gender. Control and Experimental Groups Sometimes an experiment needs a control group. Whenever you change an independent variable to see how it affects the dependent variable, you need a control group that has no change. In Example 1, the unwaxed board trials are in the control group. The waxed board trials are in the experimental group, where the independent variable has

9 been changed. An experiment could have more than one experimental group (a lot of different types of wax for example), but should have only one control group. In some experiments it is impossible to have a control group. In Example 2, it is impossible to have a group without gender, and so there is no control group and no experimental group either. Using Scientific Models in an Experiment A scientific model is used to represent something that is not easily accessible for experimentation. They are used to show structure, to simulate, to approximate, to predict, to visualize, to show relationships, and to explain. Models allow us to explain and predict what will happen in real-life situations. Models can be physical (like a model of DNA), mathematical (like an algebraic equation to show the relationship between homework completion and test scores), a diagram (like a food web), and even an idea (like natural selection). Below is a model (a diagram) of a cell membrane. This is not what the cell membrane truly looks like, but is instead a representation that allows us to understand the structure and function of a typical cell membrane. Examples of Model Use: If you want to find out what happens when a car crashes into a wall, you would probably not use real cars. Instead, you would use some sort of smaller vehicle that could react in ways that are similar to a real car. If you want to know what 8 th graders preferred learning styles are, you cannot survey all 8 th graders everywhere. Instead, you could use a sample of 8 th graders to stand for all 8 th graders.

10 Instead of using a real heart, a model can be used to show the movement of blood through the heart! Data (FYI, data is always plural, as in, The data are shown in the graph below. If you want to refer to one data point, it is called a datum). (You re welcome). J There are two types of data; qualitative and quantitative: Qualitative data uses descriptive words. An example of qualitative data is: blue, green, blue, blue, red, pink, red, gray, green, green, green (colors of shirts). In order to use this type of data, it usually needs to be made quantitative. Quantitative data uses numbers. An example of quantitative data is: 3, 4, 2, 1, 1 (number of each color of shirt). Qualitative data usually has to be put into numbers, or quantified, in order to use it. Quantitative data can be discrete (or counted) or it can be continuous (or measured). Discrete data is one measure while continuous data can have many measures in between data points. Different types of quantitative data calls for different types of graphs. Results/Raw data Raw data are the measures that you take during your experiment. They have not been manipulated or changed in any way. Raw data are reported in the results section of a lab report. They are reported using descriptive statistics like the range, median, and mode. They can be displayed in a table and graph, like below:

11 Transfiguration Exam Scores Name Gender Number Correct out of 10 Lavender F 7 Vincent M 0 Seamus M 7 Gregory M 2 Hermione F 11 Neville M 6 Draco M 8 Pansy F 5 Parvati F 7 Harry M 6 Dean M 8 Ron M 5 Luna F 9 Are these data qualitative or quantitative? If they are quantitative, are they discrete or continuous? Data Analysis Data analysis is the process of using mathematics to look for patterns to see if the data supports or does not support the hypothesis. In the example above, I might have a hypothesis that female students do better on transfiguration exams than males. To

12 see if my hypothesis is supported, I could compare the mean (average) scores of males and females. Average Number Correct on Transfiguration Exam Average Gender Score out of 10 Male 5.25 Female 7.8 There are other types of analyses that can be done to look for patterns in the data. The type of analyses that are done depend on the type of data and what you are looking for. You can analyze data using percents, ratios, range, quartiles, etc. You can also use statistical tests to figure out how confident you can be about your results. Examples include percent error to figure out how far your results are from what was expected, and t-tests and ANOVAs to determine if there really is a difference between groups. Conclusion and Discussion Once you have analyzed your data, it is time to compare your results to your original hypothesis. Use your analyses to ask yourself if the results support your hypothesis. It is okay if your results do not support your hypothesis! In fact, oftentimes, we learn more from unexpected results than we do from confirmation of what we thought! Discussion of Limitations

13 Why do (or why don t) your results support your hypothesis? Think about any limitations of your project. Limitations are any factors that were not controlled that may have affected your results. Following are some examples of limitations: small sample size (not enough trials or subjects) inadequate measuring tools unexpected factors that should have been controlled variables (such as temperature or noise during a test) differences between trials or subjects that could not have been controlled what else? Part of the discussion should also include what could be done to rectify these limitations if the experiment was done again. How could the experiment be redesigned? What different measurement tools could be used to improve accuracy? Engineering Project Design