Work on this next!
What do we know about plants from our experiences outside of school? What have we discovered in class and background research? What questions about plants interest us?
|Research Question||How will discrete and continuous phenotypic traits(Color, height, and Number of leaves) vary based on the differing fertilizer treatments?|
|Predictions||We predict that the height and number of leaves(continuous traits) will vary depending on the amount of fertilizer. The plants should be taller and have more leaves if they have more fertilizer. We also are predicting that the color will stay the same despite the differing amount of fertilizer. ...|
|Experimental Design||At least once a week we will measure the height, number of leaves, stem color, and the leaf color. Height will be measured in millimeters with a ruler, and will be measured from the very bottom to the very top. The number of leaves is simple because it can be counted. Stem color is either purple...|
|Conclusion||From the data we have collected, it seems that the plants with more fertilizer had more leaves than their lesser fertilized counterparts. the plants with more fertilizer also seem to have more yellow leaves. However, the plants with less fertilizer are, on average, taller than the others. the...|
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Thanks so much for sharing your results! Your data may not have supported your prediction, but I think you have reason to believe that, in other circumstances, you could find supportive data. I will leave you with this image of a typical yield response curve as you reflect on your experiment. It might have a few extra details, but pay attention to the x- axis of soil nutrient level (very low, low, medium, etc.) and the the y-axis, yield (which is very closely related to growth). Think about where your experimental treatments might have landed on this curve and where you might want them to be to see the greatest difference in your results.
I have enjoyed being your mentor and wish you the best on luck with the rest of the school year!
We found out that there is no significant difference between the discrete traits, and that our prediction was incorrect that the height and number of leaves are both bigger with the low fertilizer group then the high fertilizer group. Thank you
After all of our data analysis, we have not found any significant difference in any of our data. Our standard Deviation tests have proved that there is too much variance to draw any significant conclusions. Thank you so much for all the help you have given us for our project, it is greatly appreciated. We have learned a lot from this project, and we thank you for your help.
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Yesterday during class we graphed the number of leaves on a white board. we came to the conclusion that the error bars are overlapping which means there is no significant difference. We thought that might be because some of the plants died so there was no data for some plants, and there was different amounts of data from the high and low because of the plants not getting enough water and dying
We have done more standard deviation tests, and we have found the values for all of the trials. from the data collected, we made a graph which included standard error bars. since all of the bars overlapped, we found that there was never any significant difference in the data. One of the things that may have caused this was the plants not receiving enough water, or maybe the reason was because me didn't measure all of the plants, thus limiting our sample size.
These are great results that we can learn a lot from, whether they are significant or not! Since this data is from the first day of measurement, it may be the case that height at this point in time does not differ much. When plants are young, their source of nutrients may be more reliant on what was contained in the seed rather than the soil, since have not grown a large root system yet to extract nutrients. The difference between fertilizer/no fertilizer treatments may become more prominent as more time elapses. It might also be the case that your experiment was not precise enough to measure the difference after considering all experimental sources of variation, but let's talk about that after we see the rest of the results!
In class today we worked on finding the standard deviation and standard error for a practice data set of the height for one time period. We found out that the standard error was about 3 and the standard deviation was 6.8. Next we will find the rest of the standard errors and standard deviations for the rest of the data.
this type of standard deviation and standard error can be done for the height and number of leaves, but not for the color because the color isn't a numerical value
I have uploaded the data of the standard deviation to the project. here we have calculated the mean, variance, standard deviation, and standard error of the low fertilizer group on the first day. from this as well as the mean of the high fertilizer group height on the first day we measured, we have found that the results are not statistically significant. the low fertilizer group's mean was 32, and the high fertilizer group's mean was 30.1. the standard error was 1.61, and so 32- 3.22 =28.78. since 30.1>28.78, the results are not statistically significant. we plan to do this kind of analysis for the rest of the data as well.
Interesting that the plants with more fertilizer had more yellow leaves and were shorter on average. Did you or your classmates have any thoughts on why this might be the case?
Love seeing your data! Your teacher said you were also learning about statistics. What have your learned so far that you have/will apply to your experiment?
Did you notice that in your height measurements that some numbers are very low while others are very high compared to the mean? We call this variability. A measurement like 15 or 45 varies, or deviates, a lot from our average, but a measurement like 32 is not far off. As scientists, we would like all of our measurements to be close to the mean because we can be more confident that our results are closer to the true mean (or the mean we would get it we did this experiment 1000 times), but that doesn't always happen!