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Mayfly Data Variability and Analysis

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Unit Plan: Stream Ecology Lesson: 6 Time: 40 minute lesson Setting: Classroom Objectives:

Students will understand how variation in data and sample size help us to make a claim. Students will learn to use "hedging language" in discussing results.

Overview
Rating:

Main points of the lesson:

  • Variability is very important when looking at averages – scientists never look at an average without also looking at the scatter of data around that average.
  • The amount of scatter (or variability) will affect the confidence with which we can make a claim.
  • It is easier to predict outcomes when variability is low.
  • The higher the sample size the better we can see how much variability there actually is – sample size is also important when making a claim.
  • Real data is “MESSY”  but we prefer to call it  “INTERESTING”
Materials:
  • "Lesson 4 Worksheet Example" (see downloads below)
  • "Lesson 4 - Variability and Mayfly Data (powerpoint)" (see downloads below)
  • "April 2014 Mayfly Data Points" (see downloads below) - cut each row, so that each strip of paper will represent one data point
  • Large (poster size) version (laminated if possible) of the "Mayflies in Pools and Riffles ‐ April 2014" bar graph in Variability and Mayfly data powerpoint, without data points.  
  • dry erase marker or round sticky dots

Engage 

2014 data 

  • Present to students a bar graph of average mayfly data from pools and riffles from 2014.  (These should be big poster size versions of the graph “Pool and Riffles 2014” found in the PowerPoint “Variability and Mayfly Data” in the resources section. If possible, laminate the graph so students can draw on it and it can be used again.)  Discuss with students whether, based on averages they would answer the question “Is there a difference between Mayflies in pools and in riffles.”  How confident are they in their answer based on just the averages. 
  • Pass out strips of paper that have on data point from April 2014 for both pool and riffle and plot their point on the bar graph using dry erase markers.  (Use “April 2014 Mayfly Data Points”, cut spreadsheet into strips)  This will result in a bar graph that also has all the data points used to create the average.  This allows students to visualize the scatter of the points around the mean. 

The final graph with all data points should look like this: 

  • Discuss with student how their answer to the question “Is there a difference between pools and riffles” might change after each data point is added.
  • Discuss with students how scientists always use the scatter (variability) around the average to draw conclusions and discuss their data.  They never look at just averages. 
  • Often times this scatter helps us to identify outliers (data points that are very different from the rest).
  • Some people refer to data that has a lot of variability as “messy” - in fact real ecological data is usually highly variable.  Encourage students to think of it as “interesting” instead of “messy”.

 

Explore

Part 1 – This year’s data

  • Now pass out worksheets prepared ahead of time that have the data they collected on their field trip and graphs of the average number of mayflies in pools and riffles plotted on a bar graph (see example worksheet for lesson 4).
  • Have students plot their data on the bar graphs to more easily visualize the scatter of data.
  • Discuss with students their results.  Can we answer our question “Is there a difference between pools and riffles?”  How confident are we in our answer based on our data? 

 

Part 2 – Talking and writing about our results

  • Use PowerPoint to introduce the term “Hedging Language”.  Discuss how scientist use this type of language to discuss their results.
  • In writing hedging language is the use of cautious language to make a noncommittal or vague statement.
  • Show students the slide of an example graphs (Dogs and cats sleeping) and the caption that describes the graph using hedging language.

 

  • Have them write a caption for the graph of their data using hedging language.
  • To help them determine confidence in their claim have them predict where a data point would be if another kick sample was taken in both pools and riffles.  The easier it is to make the prediction the more confidence they should have in their claim (and their data).
  • Students should try to answer these questions using their graphs and data:
      1. If someone asked you where they could find mayflies in the stream what would you say?
      2. How confident would you be in your response?

 

Evaluate

The captions students write for their graphs can be used as a formative assessment to gauge understanding of the topic. 

Extend

Part 1

Provide student with mayfly data collected at the Cary Institute on 4/22/15 and 4/23/15 (below).  Have them find average for pools and riffles and plot on graph paper.  Then have them add data points to help them see the scatter of data now that they have more data. 

Have students write a caption for the graph of their mayfly data using hedging language.  Lower level students might need help with this and the exercise can be done together as a class or in groups if needed. 


School

Date

Habitat

Sample

# of Mayfly Larvae

Average

Oakwood

4/22/2015

Riffle

Kick 1

233

 

Oakwood

4/22/2015

Riffle

Kick 2

60

 

Oakwood

4/22/2015

Riffle

Kick 3

86

 

Oakwood

4/22/2015

Pool

Kick 1

27

 

Oakwood

4/22/2015

Pool

Kick 2

17

 

Oakwood

4/22/2015

Pool

Kick 3

109

 

Homeschool2

4/23/2015

Riffle

Kick 1

143

 

Homeschool2

4/23/2015

Riffle

Kick 2

216

 

Homeschool2

4/23/2015

Riffle

Kick 3

270

 

Homeschool2

4/23/2015

Pool

Kick 1

2

 

Homeschool2

4/23/2015

Pool

Kick 2

23

 

Homeschool2

4/23/2015

Pool

Kick 3

2

 

Part 2

  1. Ask the class the question “Is there a difference between the height of boys and girls in ____ grade”.  
  2. Randomly select three boys and three girls and have them stand in the front of the room. 
  3. Without actually measuring the height of each student, have the class answer the question based on their observations of the students at the front of the room. 
  4. One by one ask another boy and girl to join the group at the front.  After each new student goes to the front of the room ask if the answer to our question changes.  Discuss how our confidence in our answer gets stronger the more students there are.  Do this until all the students are lined up at the front of the class. 
  5. Pull one girl out and one boy out of the line that appear to be “average” height for the class.  Discuss with students how they would have answered the question if they only saw these two “average” students.  How confident are they in their answer, considering they know that there are some students that are shorter and some that are taller than the average student. 
  6. Explain that this is how scientists look at data.  They never look at just averages, but always look how the data is scattered around that average.  This scatter is called variability.

 

Examples of hedging language:

 

Verbs

Adverbs

Adjectives

Nouns

Phrases

seem

  • often

certain

possibility

It could be the case that

tend

sometimes

clear

assumption

It might suggest that

think

usually

probable

probability

Somewhat of a pattern

believe

probably

possible

 

Very little difference

suggest

possibly

   

a bit less

might

perhaps

   

Appear to be

could

     

Look like

 

Lesson Files:
Benchmarks for Science Literacy: 2A Patterns and Relationships 2B Mathematics, Science and Technology 9D Uncertainty NYS Standards: MST 1 - Mathematical analysis, scientific inquiry, and engineering design MST 3- Mathematics in real-world settings ELA 1- Language to collect and interpret information and understand generalizations
Next Generation Science Standards
Science and Engineering Practices: Analyzing and interpreting data Engaging in argument from evidence Obtaining, evaluating, and communicating information

Developed and written by Jen Rubbo and Andrea Caruso

Lesson was adapted from ideas presented in:
Bowen and Bartley. The Basics of Data Literacy, Helping your Students (and You!) Make Sense of Data, 2014. NSTA Press