Experimental Design B/C

[splane21]

on the offical scioly website, the experimental design reporting packet has been updated. I cant seem to find what has changed

So I don't think anything has changed although according to the update provided on January 3rd, they were supposed to remove the before box for results. i.e the updated says "The blank space asking for a results observation Before conducting the actual experiment is an error. New a form that clearly show the box shouldn’t have any information is posted on the Event Page at soinc.org."

I'm glad that this was finally fixed.

[splane21]

Hi all! I don’t know if someone’s asked this question yet because I’m still a bit new to the forums, but I’m a student coach for Experimental Design Div B and a data analyst for our Div C team but I’m kinda having trouble doing/describing the C.E.R. format replacing the Data Analysis section in part 2 of the rubric.

Can anyone help break it down for me in simpler terms? I understand how to point out specific data points and outliers in your data, but I don’t know how to really place it properly in the new reporting packet format, and it’s been really hurting my Div B teams massively during their competitions.

Thanks so much in advance!

EDIT - Before reading what I wrote below, this was posted as a FAQ a few days ago:
ON THE EXPERIMENTAL DESIGN CHECKLIST DOES STATISTICS CLAIM REFER TO CLAIMING WHAT THE STATISTICS ARE, OR WHAT YOU ARE DEDUCING FROM THE STATISTICS, OR CHOOSING WHICH STATISTICS TO BASE YOUR TREND AND INTERPRETATION OF THE DATA?
A claim is an assertion of the truth of something, typically one that is disputed or in doubt. You will then provide evidence, in this case statistics, that back up your claim. In the reasoning section, you will explain how the statistics back up your claim.

I find this section frustrating, since it is worth a large amount of points, but it seems redundant. Also the way it's worded can be a little tricky - usually I've seen the CER model applied to an entire experiment, rather than just the statistics section. Here's my (unofficial) take on it -

This year's rubric is an evolution from previous years, where section J read: "Analysis and interpretation of data." Now, the section says "Analysis of Claim/Evidence/Reasoning." I think it would be more clear if it said "Analysis of Data: Claim/Evidence/Reasoning" meaning the Claims are all about the data, not about the entire experiment.

For example, a Statistics claim may be as simple as "Our calculations for best fit/mean/median/mode are accurate." The Statistics evidence may be, "We conducted a total of 10 trials." The Statistics reasoning may be "A sufficiently large number of trials is required for statistical calculations to be considered accurate."
Outliers claim: “There are no outliers in our data.” Outliers evidence: citing the Q1/Median/Q3 data, Outliers reasoning: explain the 1.5 IQR rule.
Data Trend claim: “We would expect X to continue to increase as Y continues to increase” Data Trend evidence: cite some numbers from the data, Data Trend reasoning: “Our data suggests a direct relationship between variable X and Y”

As far as the difference between 0, 1, or 2 points is concerned, I think there’s a little subjectivity there. If I’m grading this section, I’m giving 0 to a blank response or something unrelated to the data, 1 point to a logical statement about the data, and 2 points for being both clear and complete.
I hope this helps, and if someone more experienced notices something wrong, please point it out. The scoring rubric explanation on soinc.org is currently outdated. I wish there was a “sample” lab report provided, in the 2020 rules format.

I think I mentioned this earlier. But my interpretation of the statistics claim is writing something like claim- standard deviation seemed to increase as the height the ball was dropped from decreased. Evidence- cite the evidence. Reasoning- This is because at lower heights a ball takes less time to drop to the ground and there can be more human error and variation in timing for smaller times.

I always included something similar to that in my analysis sections in previous years to get points for analysis.

However, after supervising experimental design at an invite last month I saw many teams claim "our calculations are accurate becuase..." I find that claim a little silly but since so many teams did it I suppose it is a valid way to receive points for statistics analysis, especially for division B.

[glin1011]

I think I mentioned this earlier. But my interpretation of the statistics claim is writing something like claim- standard deviation seemed to increase as the height the ball was dropped from decreased. Evidence- cite the evidence. Reasoning- This is because at lower heights a ball takes less time to drop to the ground and there can be more human error and variation in timing for smaller times.

I always included something similar to that in my analysis sections in previous years to get points for analysis.

However, after supervising experimental design at an invite last month I saw many teams claim "our calculations are accurate becuase..." I find that claim a little silly but since so many teams did it I suppose it is a valid way to receive points for statistics analysis, especially for division B.

Yeah, last weekend I was the event supervisor for the Experimental Design event at our Invitationals, and some schools did start like that.
I did notice kids are usually thrown off by how the reporting packet is this year and are confused on what to do, so kids just put random claims/evidence/reasonings to see if they can get some sort of points for it. The old data analysis part really let kids go all out instead of asking for specifics in my opinion (I really miss that section.)

I do tell my teams to at least think before putting something down, and then defending it well enough to receive some sort of point instead of an instant 0. It's honestly so much fun everytime I event supervise this one - I think this is maybe my 4th time doing it?

[lilAJ]

I'm still really confused about the Qualitative Observations section. Can anybody give an example for each section for a simple experiment(before procedure after etc.)

[Cciygcyig]

I have two questions

1. How would you express the formula for linear regression in example calculations

2. Say you had data with whole numbers, to stay consistent with sig figs would you have to make your data points something like 1.00, 2.00, etc..., or would you round all of your statistics to the nearest whole number? I don’t have much knowledge on how sig figs work because I’ve never taken chem or physics.

[Cciygcyig]

Hi all! I don’t know if someone’s asked this question yet because I’m still a bit new to the forums, but I’m a student coach for Experimental Design Div B and a data analyst for our Div C team but I’m kinda having trouble doing/describing the C.E.R. format replacing the Data Analysis section in part 2 of the rubric.

Can anyone help break it down for me in simpler terms? I understand how to point out specific data points and outliers in your data, but I don’t know how to really place it properly in the new reporting packet format, and it’s been really hurting my Div B teams massively during their competitions.

Thanks so much in advance!

EDIT - Before reading what I wrote below, this was posted as a FAQ a few days ago:
ON THE EXPERIMENTAL DESIGN CHECKLIST DOES STATISTICS CLAIM REFER TO CLAIMING WHAT THE STATISTICS ARE, OR WHAT YOU ARE DEDUCING FROM THE STATISTICS, OR CHOOSING WHICH STATISTICS TO BASE YOUR TREND AND INTERPRETATION OF THE DATA?
A claim is an assertion of the truth of something, typically one that is disputed or in doubt. You will then provide evidence, in this case statistics, that back up your claim. In the reasoning section, you will explain how the statistics back up your claim.

I find this section frustrating, since it is worth a large amount of points, but it seems redundant. Also the way it's worded can be a little tricky - usually I've seen the CER model applied to an entire experiment, rather than just the statistics section. Here's my (unofficial) take on it -

This year's rubric is an evolution from previous years, where section J read: "Analysis and interpretation of data." Now, the section says "Analysis of Claim/Evidence/Reasoning." I think it would be more clear if it said "Analysis of Data: Claim/Evidence/Reasoning" meaning the Claims are all about the data, not about the entire experiment.

For example, a Statistics claim may be as simple as "Our calculations for best fit/mean/median/mode are accurate." The Statistics evidence may be, "We conducted a total of 10 trials." The Statistics reasoning may be "A sufficiently large number of trials is required for statistical calculations to be considered accurate."
Outliers claim: “There are no outliers in our data.” Outliers evidence: citing the Q1/Median/Q3 data, Outliers reasoning: explain the 1.5 IQR rule.
Data Trend claim: “We would expect X to continue to increase as Y continues to increase” Data Trend evidence: cite some numbers from the data, Data Trend reasoning: “Our data suggests a direct relationship between variable X and Y”

As far as the difference between 0, 1, or 2 points is concerned, I think there’s a little subjectivity there. If I’m grading this section, I’m giving 0 to a blank response or something unrelated to the data, 1 point to a logical statement about the data, and 2 points for being both clear and complete.
I hope this helps, and if someone more experienced notices something wrong, please point it out. The scoring rubric explanation on soinc.org is currently outdated. I wish there was a “sample” lab report provided, in the 2020 rules format.

I think I mentioned this earlier. But my interpretation of the statistics claim is writing something like claim- standard deviation seemed to increase as the height the ball was dropped from decreased. Evidence- cite the evidence. Reasoning- This is because at lower heights a ball takes less time to drop to the ground and there can be more human error and variation in timing for smaller times.

I always included something similar to that in my analysis sections in previous years to get points for analysis.

However, after supervising experimental design at an invite last month I saw many teams claim "our calculations are accurate becuase..." I find that claim a little silly but since so many teams did it I suppose it is a valid way to receive points for statistics analysis, especially for division B.

On science olympiads website they linked a pdf (https://www.soinc.org/sites/default/fil ... Reason.pdf )that explained the CER section

[leopard953]

Hey! I wanted to ask a question about constants. Its my first year in sci oly, and im doing experimental. i am very confused about what a constant is, i know ts different for a control. Most of the experiments for compettitions this year were about time, so i would always put length of a second, but get it wrong very single time. I see here that a few ppl seem to say gravity, but what happens if your experiment wasnt related to gravity, what would you put?
Thnaks!

[knightmoves]

Hey! I wanted to ask a question about constants. Its my first year in sci oly, and im doing experimental. i am very confused about what a constant is, i know ts different for a control.

As I see it, things you control are things that you could change, but don't, and that could affect your experiment. Constants are things that are fixed by the universe, and could affect your experiment.

So gravity is a good constant - you can't change the gravity in the classroom, but if you did, your results would change. The length of a second doesn't change what happens in your experiment- it just changes the unit in which you record your answer. Room Temperature and Air Pressure are also constants.

[neerja.shah]

okay i dont know if anyone has asked this yet but what are the levels for the independent variable? anything would be helpful thank you!!

[splane21]

okay i dont know if anyone has asked this yet but what are the levels for the independent variable? anything would be helpful thank you!!

Just what your independent variables will be. For example if you do drop height and time it takes to fall to the ground, and you drop the ball from 25cm, 50cm, and 75cm, your levels for the independent variable are 25cm, 50cm, and 75cm.