Experimental Design B/C

Test your knowledge of various Science Olympiad events.
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kate!
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Re: Experimental Design B/C

Postby kate! » March 5th, 2018, 1:15 pm

Questions
1. How does an outlier in the data occur?
2. Why do we need controlled variables?
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 5th, 2018, 1:25 pm

kate! wrote:
Questions
1. How does an outlier in the data occur?
2. Why do we need controlled variables?

Answers
1. An outlier occurs when there is an error in execution, recording, etc.
2. Controlled variables help make sure only one thing is tested and the results only come from one source.

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kate!
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Re: Experimental Design B/C

Postby kate! » March 5th, 2018, 1:26 pm

dxu46 wrote:
kate! wrote:
Questions
1. How does an outlier in the data occur?
2. Why do we need controlled variables?

Answers
1. An outlier occurs when there is an error in execution, recording, etc.
2. Controlled variables help make sure only one thing is tested and the results only come from one source.

Great job, your turn.
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 5th, 2018, 1:32 pm

Questions
1. What are some examples of ways to shorten up the procedure?
2. A quantitative experiment's data is represented by a ____ graph (fill in the blank)

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kate!
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Re: Experimental Design B/C

Postby kate! » March 5th, 2018, 1:50 pm

dxu46 wrote:
Questions
1. What are some examples of ways to shorten up the procedure?
2. A quantitative experiment's data is represented by a ____ graph (fill in the blank)


Answers
1. Use diagrams and just say "repeat" instead of writing everything out.
2. Line graph.
p.s. what would a qualitative experiment be represented by?
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 5th, 2018, 4:25 pm

kate! wrote:
dxu46 wrote:
Questions
1. What are some examples of ways to shorten up the procedure?
2. A quantitative experiment's data is represented by a ____ graph (fill in the blank)


Answers
1. Use diagrams and just say "repeat" instead of writing everything out.
2. Line graph.
p.s. what would a qualitative experiment be represented by?

Correct, and for a qualitative experiment any appropriate graph excepting a line graph would work (e.g. bar, etc.)

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Re: Experimental Design B/C

Postby kate! » March 9th, 2018, 8:27 pm

New Questions
1. Write a sample rationale and hypothesis. Why is it important to have a rationale for your hypothesis?
2. Why would using a bar graph for a quantitative experiment and a line graph for a qualitative experiment not work?
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 10th, 2018, 7:35 am

kate! wrote:
New Questions
1. Write a sample rationale and hypothesis. Why is it important to have a rationale for your hypothesis?
2. Why would using a bar graph for a quantitative experiment and a line graph for a qualitative experiment not work?

Answers
1. If a ball is rolled down ramps of different ramps, then it will roll the farthest on the ramp of the greatest height because there is more potential energy at the start of a higher ramp than the start of a lower ramp, so therefore there is more kinetic energy, so the ball will roll farther. A rationale is important because it justifies your hypothesis (and it gives you points :D)
2. In a quantitative experiment, you are measuring change, and a bar graph doesn't show change. In a qualitative experiment, you are comparing the IV levels, and a line graph doesn't compare.

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Re: Experimental Design B/C

Postby kate! » March 10th, 2018, 3:15 pm

dxu46 wrote:
kate! wrote:
New Questions
1. Write a sample rationale and hypothesis. Why is it important to have a rationale for your hypothesis?
2. Why would using a bar graph for a quantitative experiment and a line graph for a qualitative experiment not work?

Answers
1. If a ball is rolled down ramps of different ramps, then it will roll the farthest on the ramp of the greatest height because there is more potential energy at the start of a higher ramp than the start of a lower ramp, so therefore there is more kinetic energy, so the ball will roll farther. A rationale is important because it justifies your hypothesis (and it gives you points :D)
2. In a quantitative experiment, you are measuring change, and a bar graph doesn't show change. In a qualitative experiment, you are comparing the IV levels, and a line graph doesn't compare.


Great answers, your turn. (welp in the last practice experiment our group did- qualitative- our math guy used a line graph... it didn't work out well, now i know why)
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 11th, 2018, 6:59 am

Questions
1. Define "operationally defined"
2. Why wouldn't "the color of the floor" or "the humidity level" work as controlled variables?

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kate!
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Re: Experimental Design B/C

Postby kate! » March 11th, 2018, 7:52 am

dxu46 wrote:
Questions
1. Define "operationally defined"
2. Why wouldn't "the color of the floor" or "the humidity level" work as controlled variables?


Answers
1. Operationally defined means how the variable works or is set up.
2. A controlled variable has to be something that is part of the materials and the experiment. Those things are outside factors and therefore are not controlled variables.
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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dxu46
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Re: Experimental Design B/C

Postby dxu46 » March 11th, 2018, 10:04 am

kate! wrote:
dxu46 wrote:
Questions
1. Define "operationally defined"
2. Why wouldn't "the color of the floor" or "the humidity level" work as controlled variables?


Answers
1. Operationally defined means how the variable works or is set up.
2. A controlled variable has to be something that is part of the materials and the experiment. Those things are outside factors and therefore are not controlled variables.

Correct, your turn.

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kate!
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Re: Experimental Design B/C

Postby kate! » March 12th, 2018, 11:41 am

Questions
1. What should be the last step of your procedure?
2. What is the difference between qualitative observations and experimental errors?
3. Why should you include standard deviation in the statistics section? Also, why should statistics that are not basic statistics (mean, median, mode, etc.) be included?
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!

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Re: Experimental Design B/C

Postby OrigamiPlanet » March 18th, 2018, 9:43 pm

kate! wrote:
Questions
1. What should be the last step of your procedure?
2. What is the difference between qualitative observations and experimental errors?
3. Why should you include standard deviation in the statistics section? Also, why should statistics that are not basic statistics (mean, median, mode, etc.) be included?


Answers
1. Clean up your materials from the experiment.
2. Qualitative observations are notes that one should take account of as data that isn't really expressed as numerical sets of data. Experimental errors are observations that will take effect on quantitative data, and should be considered more towards the accuracy of quantitative data.
3. Standard deviation provides us a basis of how great of a difference there was in the data, so that one can know the average variation between points; knowing this would help to determine data's validity. Other more complicated statistics are also very good for analysis, as they provide insight on accuracy of data, rather than just proving overall trends. For example, the Goodness-of-Fit test (sidenote don't actually try this since it is a bit time consuming) can prove whether or not there is a significant difference for sure or not by determining the odds every value is equal to the mean. Knowing this then grants us the opportunity to look at trends and whether or not they can actually be taken seriously for consideration.
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Re: Experimental Design B/C

Postby kate! » March 19th, 2018, 11:56 am

OrigamiPlanet wrote:
kate! wrote:
Questions
1. What should be the last step of your procedure?
2. What is the difference between qualitative observations and experimental errors?
3. Why should you include standard deviation in the statistics section? Also, why should statistics that are not basic statistics (mean, median, mode, etc.) be included?


Answers
1. Clean up your materials from the experiment.
2. Qualitative observations are notes that one should take account of as data that isn't really expressed as numerical sets of data. Experimental errors are observations that will take effect on quantitative data, and should be considered more towards the accuracy of quantitative data.
3. Standard deviation provides us a basis of how great of a difference there was in the data, so that one can know the average variation between points; knowing this would help to determine data's validity. Other more complicated statistics are also very good for analysis, as they provide insight on accuracy of data, rather than just proving overall trends. For example, the Goodness-of-Fit test (sidenote don't actually try this since it is a bit time consuming) can prove whether or not there is a significant difference for sure or not by determining the odds every value is equal to the mean. Knowing this then grants us the opportunity to look at trends and whether or not they can actually be taken seriously for consideration.


Great answers, your turn.
Last year I knew stuff about rocks, minerals, experiments, and ecosystems, yay!
Now I know stuff about amphibians, reptiles, water, and more experiments, yay again!
I'm planning to learn stuff about oceanography, fossils, water, and birds, yay for the third time!


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