Photosynthesis II Lab: Data Analysis and Clean-Up Instructions

Abstract

REMINDERSComputer access – you will need access to the internet, Microsoft Excel (or Google Sheets)Supplemental videos – Three supplemental videos will be Panopto. Lab6_PhotosynthesisII_Part1_DataEntry_WebBio121Lab6_PhotosynthesisII_Part2_Calculations_WebBio121Lab6_PhotosynthesisII_Part3_Graphing_WebBio121REMINDERSComputer access – you will need access to the internet, Microsoft Excel (or Google Sheets)Supplemental videos – Three supplemental videos will be Panopto. Lab6_PhotosynthesisII_Part1_DataEntry_WebBio121Lab6_PhotosynthesisII_Part2_Calculations_WebBio121Lab6_PhotosynthesisII_Part3_Graphing_WebBio121Lab 6 – Photosynthesis II – Data Analysis REMINDERS Computer access – you will need access to the internet, Microsoft Excel (or Google Sheets) Supplemental videos – Three supplemental videos will be Panopto. Lab6_PhotosynthesisII_Part1_DataEntry_WebBio121 Lab6_PhotosynthesisII_Part2_Calculations_WebBio121 Lab6_PhotosynthesisII_Part3_Graphing_WebBio121 REMINDERS Computer access – you will need access to the internet, Microsoft Excel (or Google Sheets) Supplemental videos – Three supplemental videos will be Panopto. Lab6_PhotosynthesisII_Part1_DataEntry_WebBio121 Lab6_PhotosynthesisII_Part2_Calculations_WebBio121 Lab6_PhotosynthesisII_Part3_Graphing_WebBio121 CLEAN UP FOR LAB 3Lamp – After 3 weeks of collecting data, you are done with the lamp/light bulbs for lab. Pinto beans – You may save/cook/eat the remaining pinto beans. You may also compost them. Plants – Plants/dirt can be (1) kept – you can continue growing your own pinto beans OR (2) compost/kitchen scrap pile OR (3) thrown away in the trash. Pots/solo cups – Once plants have been removed, please recycle (or wash and reuse) solo cups. CLEAN UP FOR LAB 3Lamp – After 3 weeks of collecting data, you are done with the lamp/light bulbs for lab. Pinto beans – You may save/cook/eat the remaining pinto beans. You may also compost them. Plants – Plants/dirt can be (1) kept – you can continue growing your own pinto beans OR (2) compost/kitchen scrap pile OR (3) thrown away in the trash. Pots/solo cups – Once plants have been removed, please recycle (or wash and reuse) solo cups. CLEAN UP FOR LAB 3 Lamp – After 3 weeks of collecting data, you are done with the lamp/light bulbs for lab. Pinto beans – You may save/cook/eat the remaining pinto beans. You may also compost them. Plants – Plants/dirt can be (1) kept – you can continue growing your own pinto beans OR (2) compost/kitchen scrap pile OR (3) thrown away in the trash. Pots/solo cups – Once plants have been removed, please recycle (or wash and reuse) solo cups. CLEAN UP FOR LAB 3 Lamp – After 3 weeks of collecting data, you are done with the lamp/light bulbs for lab. Pinto beans – You may save/cook/eat the remaining pinto beans. You may also compost them. Plants – Plants/dirt can be (1) kept – you can continue growing your own pinto beans OR (2) compost/kitchen scrap pile OR (3) thrown away in the trash. Pots/solo cups – Once plants have been removed, please recycle (or wash and reuse) solo cups. Objectives: Today in lab, we will accomplish two tasks: You will independently summarize your own data in a downloadable Excel sheet posted on Sakai Lab6_PhotosynthII_IndividualStudentDataWorkbook_WebBio121 Students will use their Excel skills from Lab 3 to perform data analysis on their data. Students will calculate the values needed for one table and one graph for their scientific poster. Often, the most difficult part of any science experiment is organizing and manipulating the data you have collected – we refer to this process as “data analysis”. This process is often dictated by how much data was collected – the more data you have, the more complicated the process tends to be. The data collection from Lab 3 was relatively simple – each student collected height data, sprout success data, and time to first sprout data for three weeks on four pots of seeds. This means, that our sample size (or how big our data collection sample is) is the total number of plant pots per light that contributed to the experiment. Recall that our question from Lab 3 was: Does light wavelength affect bean sprouting success, sprouting speed and growth rate? You have spent the last three weeks collecting data that will help your class to answer this question. To analyze our data, we will compare mean values among our treatment groups, and determine whether they are significantly different (i.e. have a difference that can be statistically proven) by looking at the overlap on the standard error of each treatment group. Standard error is the standard deviation of the sampling distribution of a statistic. Differentiating between standard deviation and standard error can be confusing – they are not the same thing. Standard deviation measures the amount of variability in a dataset, and standard error is an estimation of the standard deviation of all possible datapoints in a dataset. To calculate standard error, you will need to first calculate standard deviation. This is not a statistics class, so let us unpack the definition of standard error a little bit. Standard errors are usually presented with means, or averages. Now, when an experiment is performed, it is almost always performed on a subset – or sample – of a population. That is to say, the Lab 3 experiment is on Pinto Beans, but it would be literally impossible for us to measure and take the average heights of every single Pinto Bean that is growing or will ever be grown in the world, so what we are really experimenting on is a sample of Pinto beans. Because our sample size of Pinto Beans is a drastically different size than a sample of all of the Pinto Beans that exists, it will have an average height that is different from the overall sample. Standard error is a way to know how close the average of your sample is to the average of the entire sample. To calculate standard error, divide the standard deviation (the average distance that a sample is from the average) by the square root of the sample size. In most research, the symbol for sample size is a lowercase “n”. How we will use standard error in Bio 121: if the standard errors of two values overlap, those two things are not statistically different. If they do not overlap, they are statistically significantly different (aka the results are significantly different). For example, Table 1 and Figure 1 show the results of an example experiment with three treatments: Table 1. Means and standard error from Example Experiment Treatment Mean +/- Standard Error A 5 2 B 6 1 C 9 1 Figure 1. Diagram of Example Experiment data from Table 1: Treatments A, B, and C +/- one Standard Error. Shown on the number line in Figure 1 are the results from the hypothetical experiment, with the averages graphed plus and minus one standard error (SE). From Figure 1, we can see that the SE of A and B overlap. We would say that A and B are not statistically different. Because their standard errors overlap, they are too statistically similar. We can also see that the SE of C does not overlap with either A or B. Thus, we would say that C is statistically different from both A and B because their SEs do not overlap. This is how we will use standard error in our class – to assess whether our results are statistically different or not. The more the standard errors of two things overlap, the more similar they are, and the less the standard errors of two things overlap, the more different they are. One thing to note is that sometimes two things that we expect to be statistically different end up with slightly overlapping standard errors – which is to say that the data would indicate they are not statistically different. This does not mean that the data are incorrect – data doesn’t have an ulterior motive, data just conveys the reality of what was measured. In cases like this, it could mean that our sample size was not large enough to see an effect of our treatment – generally, the larger your sample size (i.e. the closer a sample size is to sampling everything that could be sampled), the more likely it is that your data are presenting the true picture, or alternatively, the relationship we expected to see may not actually exist (more experimentation may be required to rule out one or the latter). More data by way of replicating...