Ch 11. Scientific Method & Reasoning

Science

is a system used to gain knowledge based on observations, testing and reasoning

accuracy

refers to how close a measurement is to the 'true' value

Why evaluate data?

ccientific data can influence policymaking and other important decisions, so having good data is key. Trustworthy data lead to better informed decisions, greater scientific credibility and can even point out where your study may need to be tweaked or redesigned.

What are the different types of error?

human error random error systematic error drift

What are the steps of the scientific method?

question -a scientist proposes the problem that he or she wants to solve hypothesis -a potential answer to the question at hand. Sometimes, hypotheses look more like predictions. experiment - a test. investigations that are intended to prove or disprove a hypothesis. Important data comes from performing an experiment observation -the results that he or she gets from the experiment analysis - involves comparing the results of the experiment to the prediction posed by the hypothesis. Based on the observations he or she made, the scientist has to determine whether the hypothesis was correct. conclusion -a scientific process is a statement of whether the original hypothesis was supported or refuted by the observations gathered. *remember sometimes the steps don't always appear in the same order. Sometimes observation comes first before questions.

peer review

scientists critique each other's work and decide whether it meets the standards of the scientific community.

observations

sensory experiences that lead scientists to ask questions and seek answers about natural phenomena. While many observations are seen or heard, they can also occur through touch, smell, and more. For example, you might hear two birds singing to each other, or you might notice that blood has a metallic taste. You might feel your skin's hairs stand up when you get cold, see certain flowers in your garden closing up at night and re-opening in the morning, or smell a strong odor of a skunk in your backyard one evening. All of these are observations. You are seeing, hearing, tasting, feeling, and smelling the world around you, and these observations should lead you to wonder about these occurrences and how you can find answers to your questions. Why do the birds sing to each other that way? What component of blood makes it taste metallic? Why do your hairs stand up on end? How do your garden flowers know the day is over?

p-value

tells you is the percentage that the results are caused by random chance. When testing for statistical significance, you want below a .05 p-value. If you do have below a .05, you are able to reject the null hypothesis. If your p-value is above .05, then you fail to reject the null hypothesis. Often, when writing up the results of an experiment you will use the phrase 'statistically significant' or 'not statistically significant'. This lets you know whether or not the experiment demonstrated a difference, relationship, or interaction. This is at the heart of hypothesis testing. There are dozens of statistical tests to run, and nearly all of them have a statistically significant component to let you know if the differences are viable.

retrograde motion

the apparent backwards motion of a planet against the background of stars. Example: Imagine you're on a circular racetrack, riding in a really fast car. You know that everyone on the racetrack is moving forwards in the same direction. But since you are going really fast, as you overtake slower cars, they appear to move backwards, away from you. Clearly, they are not doing this, but because you are moving faster than they are, it seems to be so.

Statistically significant

the difference in the results did not occur by random chance. This is almost always represented by a lower case 'p'. Another term you may also hear for this is alpha, and it may be represented by the alpha symbol (the one that looks like a little fish).

geocentrism

the idea that Earth is at the center of the universe

alternative hypothesis

the prediction that there is a measurable interaction between variables The symbol for the alternative hypothesis is HA Example: Null says, 'I bet that if you drink the beer here that nothing will happen!' To which Alt replies, 'I bet the beer here will get you drunk!' The two scientists drink long into the night until Null blacks out and, because he passed out, was rejected by the bar patrons and thrown out into the dumpster.

null hypothesis

the prediction that there is no interaction between variables The symbol for the null hypothesis is H0 Example: Null says, 'I bet that if you drink the beer here that nothing will happen!' To which Alt replies, 'I bet the beer here will get you drunk!' The two scientists drink long into the night until Null blacks out and, because he passed out, was rejected by the bar patrons and thrown out into the dumpster.

hypothesis

uses these questions and observations from a scientific method to form an idea that can be tested

replication

Performing certain steps or even the entire experiment over and over again gives us more information than what we get in just one pass. But even if you very carefully and thoroughly perform the exact same steps each time, you may find that you get different results. Does this mean you have bad data? Not necessarily. But it might help you identify different types of errors associated with your data.

scientific method

an organized way of studying something and to solve problems. The scientific method involves asking questions and making observations.

human error

any basic human mistake: spilling a substance, dropping equipment, forgetting to turn off the drying oven, etc. These errors can and will happen, no matter how avoidable they may seem in hindsight!

precision

which is often called reproducibility or repeatability, is the agreement of repeated measurements. Example: You are throwing darts, but you are nowhere near the center, but at least you hit the same spot every time. In this case, we can say that while you were not accurate, you were precise. You can also be accurate and precise hitting the center over and over again. or neither accurate and precise.

subjective process

your conclusions are your opinions. How you interpret your data depends on a number of factors. For example, your background and education, the types of analyses you performed, and even your motives for performing the experiment all play a role in determining the conclusions you draw from your data. scientists still debate with other scientists that global warming is real.

drawing logical conclusions

which is just evaluating information and making appropriate judgments Think about how you get dressed in the morning. What you wear depends on a number of different factors. What will the weather be like that day? Will it be hot or cold? Is it going to rain? Will you be going into the office or doing something else? Let's say, for example, that it is winter and it is a Saturday. During the winter, it is cold, and on Saturdays you take your dog to the park. Based on this information (the data), you can say with a good amount of certainty that you will need to wear cold-weather clothing, such as warm socks, long pants, and a coat. You will also need to plan for being outside, so you decide to wear a hat, gloves, and maybe even a scarf. What about when you make lunch? Here, you need to consider what food you have in your fridge, how much time you have to cook, what you already ate for breakfast (because who wants to eat the same meal twice in a row?), as well as if you have dietary restrictions (like being a vegetarian). Based on all of this information, you can come to a logical conclusion about what to have for lunch that day. These are, of course, very simple examples. But, can you see how pieces of information take on new meaning when you put them into context and draw some conclusions from them? Let's say you ran an experiment to better understand plant growth. You had three groups of plants: one that received fertilizer and two that did not. One of the non-fertilized groups received three more hours of sunlight exposure than the other two groups of plants, and the third group of plants received nothing. In the end, the group that received the fertilizer had the greatest amount of growth, the group that received neither fertilizer nor extra sunlight had the least amount of growth, and the group that received no fertilizer, but did receive extra sunlight was somewhere in between. What conclusions might you draw from these results? You might say that both fertilizer and sunlight led to an increase in plant growth since both of these groups grew more than the group that received nothing. You might also conclude that fertilizer had a greater impact on growth than sunlight, since the fertilizer group grew more than the extra sunlight group. Based on the preliminary data, these conclusions make sense. But now your results are so much more meaningful because, instead of just presenting some arbitrary plant growth values, you have explained what the numbers mean in a given context. Additionally, you might also conclude that further studies are still needed because now you have more questions. Such future studies might test whether different types of fertilizers affect plant growth differently, or if the amount of fertilizer plays a role in plant growth. You might also test sunlight alone (leaving fertilizer out of the experiment) to determine the optimal amount of extra sunlight for maximum plant growth.

Charles Darwin

English natural scientist who formulated a theory of evolution by natural selection (1809-1882)

Occam's razor

The philosophy that the simplest explanation is usually the correct one *The reason the word razor is used is because we use this principle to shave away extraneous details from an explanation for something.* Example: Let's say that you came home one day and found that the stove was on. This surprises you since you're normally very diligent about not forgetting to turn it off. One explanation for this is that you left it on after cooking earlier and simply had a brain lapse, even if you don't want to admit to it. Another explanation may be that someone broke into your home while you were gone and turned the stove on, so it's not your fault the stove is on. The second explanation means someone had to know how to pick your door lock, disarm your alarm system, avoid detection by neighbors, clean up any evidence of entry such as fingerprints, turn the stove on, and leave just as quietly without being detected. Not to mention, you have to come up with some sort of motivation for this intruder to do such a thing. The first explanation only requires a brain lapse and a lack of action on your part to turn the stove off. Therefore, in the absence of any evidence to the contrary, the first explanation is the simplest and the one that is most likely to be correct.

drift

This is when an instrument gradually changes over time. Drift often occurs in instruments that record continuously, like detectors. Each type of instrument will have a different amount of drift - for example, some bat detectors drift about eight minutes per year, while some fish detectors drift a few seconds each month. A few minutes or seconds may not sound like a lot, but if you're trying to use those detection data to calculate swimming speeds in meters per second, you need the seconds to be the right ones!

Who developed the principle of Occam's razor?

a Franciscan philosopher named William of Ockham (1285) 'Plurality should not be posited without necessity.'

inductive reasoning

drawing conclusions from evidence. This means a scientist collects data and interprets it. Example: A researcher, let's say you in this example, was taking a test and noticed that this fly kept buzzing around. It buzzed around your head and it was distracting you. So you wonder if noise distraction has any effect on test taking. You will then set up an experiment involving 100 people taking a test with some noisemaker in the background. The people will be divided into five groups of 20, and each group will have a different level of noise, from quiet to obnoxiously loud. After all five groups have completed the tests you will compare their different scores to see if there was a difference. If the scores typically grew steadily worse as the noise increased, then you could draw a conclusion that as distractions increase, test scores will generally decrease. If, on the other hand, the majority of their scores increased with the noise, then you would make the correlation that as distraction increases, test scores will generally increase.

Systematic errors

error associated with the instruments. Perhaps your pipette is not dispensing the correct amount of fluid, or your scales are not calibrated correctly. In this case, you would have the same error each time because your instrumentation is off by the same amount for each measurement. In the end, all your measurements would be too high or too low, leading to precise, but inaccurate, data.

random error

errors that occur randomly in space and time are both unavoidable and unpredictable

Point out the significance of the final step of a research project

explaining what it all means. This final step is so critical because it allows you as the researcher to bring it all together. This is the time to explain not only what your data mean for your experiment but also what they mean for future scientific studies and the benefits they provide for the scientific field. The last step also allows you to describe what you learned from the experiment and any pos (the final part of your paper after you have formulating your hypothesis, designing your experiment, and analyzing your data.)

deductive reasoning

finding evidence to support or disprove conclusions. This is where a scientist has an idea and then tests it to see how correct it is. Let's say every time you run the experiment with the noise making and test taking you find that as noise increases, the test scores decrease. Your observations, however, indicate that two or three people per group do substantially worse in quieter conditions but extremely well in very noisy conditions. When you look at their demographic information, you see that most of them admit to having Attention Deficit Hyperactive Disorder, or ADHD. With your observation, you develop the conclusion that individuals with ADHD have an opposite reaction to noise compared to those without ADHD. To confirm or disprove your conclusion, in the next round of test taking you select a higher number of individuals with ADHD and compare their scores to those without. As a researcher, you created a conclusion about the effect of ADHD on test taking. You then collected your evidence that would support or disprove your conclusion. Your evidence indicates that there is no connection between ADHD, test taking and noise levels. The conclusion was proven false. Deductive reasoning is having a conclusion and then testing your conclusion to prove or disprove it.