Activity 1 — Measuring Leaves and Entering Data

Field observations, hypotheses, and organizing data

scientific-method
data-collection

Head outside to collect leaf samples, practice inductive and deductive reasoning, formulate a testable hypothesis, and organize the resulting data for entry into a spreadsheet.

Author

Bill Perry

Published

September 10, 2026

How to use this worksheet: This is a field + discussion worksheet — no coding today. Work in your assigned group. Fill in every blank before you leave lab; you will need these answers again in Lecture 2 and in the Extension.

Objectives

  • Ask a testable question and turn it into a null and alternate hypothesis
  • Identify dependent and independent variables
  • Decide, as a class, how to standardize a field measurement
  • Design a spreadsheet — column names, units, and metadata — before you collect data
  • Make a first, quick look at the data in Excel

Part 1 · Build the question (do this before you go outside)

Our observation: trees growing in shady or crowded conditions sometimes seem to have larger leaves, as if the leaf is compensating for lower light.

Your question:

_______________________________________________________________________

Your prediction (what do you think will happen, and why?):

_______________________________________________________________________

Null hypothesis (H₀) — the “no difference” statement:

_______________________________________________________________________

Alternate hypothesis (Hₐ) — the statement we think might be true:

_______________________________________________________________________

Dependent variable(s) (what you will measure):

_______________________________________________________________________

Independent variable (what you are comparing/grouping by):

_______________________________________________________________________
Tip

🖐 Check with your group before moving on

If two group members wrote different hypotheses, talk it through — you need to agree as a group before you collect data.

Part 2 · Design the sampling (decide as a class)

Answer these as a class discussion, then write down the class’s decision — you must all follow the same protocol or the data cannot be combined.

  • How many trees will each group sample? ____
  • How many leaves per side, per tree? ____
  • What counts as “sunny side” vs. “shady side”? Write the rule the class agreed on:
_______________________________________________________________________
  • What counts as a “mature” leaf (not a bud, not damaged)?
_______________________________________________________________________
  • What height on the tree will you sample from, and why does that matter?
_______________________________________________________________________
  • Any other rule your class agreed on to keep everyone’s measurements comparable:
_______________________________________________________________________

Part 3 · Go outside — collect

  • Head to the field site with your group
  • Collect 3 mature leaves from the sunny side and 3 from the shady side of each tree, on at least 1 tree per group, following the class protocol above
  • For each leaf, trace its outline onto a sheet of white copier paper and label the tracing with the tree ID and side (sunny/shady) — we will use these tracings later (weight of paper predicts leaf area)
  • Bring everything back to lab: leaves, tracings, and your field notes
Important

⚠️ Watch out!

Label every tracing immediately — a pile of unlabeled leaf outlines is useless data. Write the tree ID and side on the paper before you trace the next leaf.

Part 4 · Back in the lab — decide what and how to measure

As a group, decide:

  • What will you measure on each leaf? (mass, length, width, area from the tracing, …) List them:
_______________________________________________________________________
  • What tool and units for each? (calipers or a ruler → mm, balance → g, …)
_______________________________________________________________________
  • What will you do if a leaf is torn, chewed, or otherwise damaged?
_______________________________________________________________________

Now measure your leaves and record the raw numbers here (or directly into your spreadsheet if you are ready):

Tree ID Side (sunny/shady) Leaf # Measurement 1 Measurement 2 Measurement 3

Part 5 · Build the spreadsheet

Open Excel and set up your data sheet. Decide as a group:

  • Column names — no spaces, lowercase, units in the name (e.g. leaf_mass_g, not Leaf Mass (g)). Write your planned column names:
_______________________________________________________________________
  • One row per what? (Hint: one row per observation — one leaf)
_______________________________________________________________________
  • Where does the metadata go? Metadata = data about your data: who collected it, what date, what tree/site, what instrument. Decide where in the workbook this lives (a separate metadata tab is common) and what it will record:
_______________________________________________________________________

Enter your measurements into the spreadsheet using your agreed column names.

Tip

Best practice

Keep your raw entered data untouched in one tab. If you need to fix or recode something, do it in a copy — never overwrite the original numbers you measured.

Part 6 · A first look — make a quick Excel chart

  • Make a simple chart in Excel comparing sunny vs. shady leaves (any one measurement is fine for now)
  • What did you put on the X axis? On the Y axis?
_______________________________________________________________________
  • Based on this rough look, does your data seem to support Hₐ or H₀ so far?
_______________________________________________________________________

Before you leave

Note

📚 Suggested reading before Lecture 2

Whitlock & Schluter, Ch. 1 — Statistics and Samples. It covers exactly what we practiced today: turning an observation into a testable question, and why we sample instead of measuring every leaf that exists.

Extension — out of class (~30–40 min)

Your first solo R session. Put all of it in a script — scripts/01_first_look.R — that you turn in, with a # comment at the start of each code chunk. Use the leaf dataset from class (data/2026_09_03_data_sci_leaf_area.xlsx); download it again from Activity 02 if you don’t have it. Everything you need was in Lecture/Activity 01–02.

E1 · Load and plot (4 pts)

# ---- Extension: first look at the leaf data ----
library(readxl)
library(tidyverse)
library(janitor)

leaf_df <- read_excel("data/2026_09_03_data_sci_leaf_area.xlsx") %>%
  clean_names()

glimpse(leaf_df)

# scatter: does petiole length go with leaf mass? colour by shade
leaf_df %>%
  ggplot(aes(x = petiole_mm, y = mass_g, color = shade)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(x = "Petiole length (mm)", y = "Leaf mass (g)", color = "Shade") +
  theme_minimal()

Save the plot to figures/ at dpi = 300. In one sentence each (as # comments in your script): do leaves with longer petioles tend to be heavier? Which side looks heavier, just from the plot?

E2 · Predict, then check (3 pts)

  1. From memory of your E1 scatter, describe in a comment what a boxplot of mass_g by shade will look like — two rough boxes, where the middles and spreads fall. No peeking.
  2. Now run it (new code — work it out from your geom_point() code): leaf_df %>% ggplot(aes(x = shade, y = mass_g)) + geom_boxplot()
  3. Compare your description to reality: right about which side is higher? more spread? What surprised you?
  4. Pick any row number 1–53 and run leaf_df %>% slice(<that number>). Write that one leaf’s shade, mass_g, petiole_mm, and thickness_mm — your leaf.

E3 · Connect it to the science (3 pts)

In plain language, as if to a friend who’s never taken a science class:

  1. Using your leaf from E2, is it typical for its side or unusual? Why?
  2. Why measure many leaves per side instead of one? Use an analogy to something outside science.
  3. Why might shady leaves need to be larger to survive? (What does a leaf do, and what does it have less of on the shady side?)

Next time: we bring this spreadsheet into R and build our first plot as a class.