Determine which of the following observations are testable. For those that are t

Question

Determine which of the following observations are testable. For those that are testable:
· Write a hypothesis and null hypothesis
· What would be your experimental approach?
· What are the dependent and independent variables?
· What is your control?
· How will you collect your data?
· How will you present your data (charts, graphs, types)?
· How will you analyze your data?

1. When a plant is placed on a window sill, it grows faster than when it is placed on a coffee table in the middle of the living room.


2. The teller at the bank with brown hair and brown eyes and is taller than the other tellers.


3. I caught four fish at the seven o’clock in the morning but didn’t catch any at noon.


4. The salaries at Smith and Company are based on the number of sales and Billy makes 3,000 dollars more than Joe.


5. When Sally eats healthy foods and exercises regularly, her blood pressure is lower than when she does not exercise and eats fatty foods.


6. The Italian restaurant across the street closes at 9 pm but the one two blocks away closes at 10 pm.


7. Bob bought a new blue shirt with a golf club on the back for twenty dollars.


8. For the past two days the clouds have come out at 3 pm and it has started raining at 3:15 pm.


9. George did not sleep at all last night because he was up finishing his paper.


10. Ice cream melts faster on a warm summer day than on a cold winter day.


11. How can you apply scientific method to an everyday problem? Give one example.

Explanation / Answer

I won't give away the answers here, but I'll tell you how to get there. If the phrase is a testable observation, it cannot simply be a statement of something that happened or is. It has to be something you can *test* . Not a question, not a simple statement of point occurrences, but a testable generalization or a phenomenon/trend of certain actions/behaviors/happenings. A hypothesis is a proposed explanation based on facts, sometimes also called a suggested solution. ex. If cars stop for passersby at the crosswalk and I walk across the crosswalk, cars should stop for me. The "if" statement is my statement of fact. The "and" is my method of testing. The "cars should stop for me" part is the suggested solution. Together, they form the proposed explanation of the phenomena of cars stopping for passersby. A null hypothesis is a separate hypothesis that deals with the STATISTICAL aspect of the phenomenon, in the situation where there is no relationship between the factors being tested. For example, for the same situation with the cars, if the statement was something like "When passersby cross the street on crosswalks, cars stop for them", the *hypothesis* could be what I suggested above, but the null hypothesis could suggest two solutions: 1. cars stop for passersby everywhere, or 2. cars don't stop for passersby anywhere. The point there is that the suggested solutions must convey NO relationship between the factors being tested. The experimental approach is rather open ended. Choose a way to test your hypothesis, using some quantifiable tools. Anything from rulers, stopwatches, or simple counting to complicated lab equipment, if it fits the question. The control group is how you obtain a comparison. In drug trials, the control group is often the one that gets a placebo. The point is that when they compare the group that was treated to the one that wasn't, the only differences should be due to the effects of the drug (not other factors that come from people just thinking they're getting better because they're getting treatment/factors that aren't directly accounted for in the tests). That's what makes for a good experiment. How you'll collect data, again, is fairly open ended. In the end, it usually comes down to keeping a good lab notebook along with whatever else you're using. Presenting and analyzing the data depends on what data you collect. If you have a comparison of subject data, look into T-tests. If it's measuring effects of a treatment on more than 2 groups, look into the ANOVA. (Those are statistical tests. Not sure if you need that much detail.) Basic statistics (mean/median/standard deviation/confidence interval) can also be useful. Analyzing

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