Microgreens Crop 2 Data Analysis

To-Do Date: Oct 30 at 11:59am

Data Entry Links to an external site.

 

In lab we harvested our microgreens and entered the data in this Google Sheet. You will each need to make a copy of this Google Sheet and use it for data analysis.  Here is a brief explanation of what we are trying to achieve by data analysis and how to do so.  Each student will need to submit this data analysis as part of their final microgreens report. 

What is Data Analysis

Sometimes, it is possible to look over a list of measurements and get a good idea of what the information is trying to tell us.  But for the most part, it is going to look like a jumble of numbers.  Data analysis is the process of processing the results to that we can see patterns and draw conclusions.   Obviously this is  a large and deep topic, but we are going to look at three fairly simple ways to analyze data

    • Calculating the average
    • Visualizing using graphs
    • Probability of being wrong- 

 

Calculating the Average 

In lab we will go over how to do this calculation.  I will insert a video below that will explain the process. Here are some important first steps

After everyone has entered their data in the spreadsheet, make your own copy of the data and put it in your own Google Drive

 

Visualizing your Data

It is much easier to see patterns when examining the average amount of growth for each treatment, but it is even more useful to put those numbers into a visual form, i.e. a graph. I will insert a video that goes over how to make a bar graph from our microgreens data. 

 

Probability of Being Wrong

Our goal is to decide if our original hypothesis is correct or not.  The simplest way to do that is to compare the average amount of microgreens in the experimental vs. control treatment groups. However the average doesn't tell us if there was a lot of variation in our data. That could undermine our confidence in  deciding that there is a difference.  Using statistical analysis, we can calculate the probabliity of being wrong, called the p-value. In biology, we only accept our hypothesis if the probability of being wrong is less than 5%. 

Watch this video to learn about p-values

In this video, I explain how to calculate the averages and graph your data