Week 1 Data Lab: Meet the Penguin Field Log

Part 1 — The Big Picture

This week you learned that who was measured, and how, comes before what was found. In this lab you open the real penguin field log yourself, count what it holds, sort every column by type and level of measurement, and decide what kind of study it is. A chatbot then does the same job, and you catch where it goes wrong.

Time budget: Plan for about 90 minutes.

Part 2 — The Task

Guiding question: What does each column of the penguin field log record, how complete is it, and what can a study like this conclude?

Workshop data: palmer-station-penguins

Get the data: palmer-station-penguins landing page or the direct data file — free, no login required.

Source: Measurements of 344 penguins of three species on three islands of Antarctica's Palmer Archipelago, recorded by field researchers and republished in the palmerpenguins package.

• Rows: 344 penguins
• Columns: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, year
• Missing values: Numeric measurement columns and sex carry missing values for a small number of penguins

Part 3 — Materials & Procedure

Tools and materials

• Google Sheets (free) or Excel
• The palmerpenguins CSV file
• A document or notebook for your written answers

Ways of working

• interpret-real-data
• compute-and-plot
• structured-observation

Procedure

1. Open a blank sheet. In Google Sheets, click cell A1 and type =IMPORTDATA("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/inst/extdata/penguins.csv"). In Excel, save the file from that address, then use Data, then From Text/CSV, and load it starting in cell A1. Tool: Google Sheets (free) or Excel. Check: Row 1 shows the eight column names, and the penguins fill rows 2 through 345.
2. In an empty cell, type =COUNTA(A2:A345) to count the penguins in the species column. Tool: Google Sheets (free) or Excel. Check: The cell shows 344.
3. The file writes a missing value as the text NA. For each of the eight columns, count its missing values with =COUNTIF(range,"NA"), for example =COUNTIF(G2:G345,"NA") for sex, and record each count in the first scaffold table. Tool: Google Sheets (free) or Excel. Check: Sex shows 11; each of the four measurement columns shows 2; species, island, and year show 0.
4. Count each species with =COUNTIF(A2:A345,"Adelie"), then the same for Chinstrap and Gentoo, and add the three counts. Record them in the second scaffold table. Tool: Google Sheets (free) or Excel. Check: The three counts add to 344.
5. Read down each column and fill in the rest of the first scaffold table: what the column records, categorical or quantitative, discrete or continuous for quantitative columns, and its level of measurement, with a one-line reason. Check: Every one of the eight rows has a type, a level, and a reason.
6. Answer the analysis questions in your document, then paste your two tables into an approved chatbot with the prompt in Part 6 and check its answer against the scripted checks. Tool: A document or notebook for your written answers. Check: You have five written answers and a list of the chatbot's errors with corrections.

Fallback route

When to use it: Use this route if the file will not import or no spreadsheet is available.

Work from the model data below, which reports each column's example values and its missing count from the file, together with the species counts. Complete both scaffold tables, every analysis question, and the critique step from it, and submit the same items for equal credit.

This route earns the same credit as the primary route.

These summaries of the real file give you every count and example value the lab needs without importing it.

Fallback model data

Column	Example values from the file	Missing (NA) count
species	Adelie, Chinstrap, Gentoo (152, 68, and 124 birds)	0
island	Biscoe, Dream, Torgersen	0
bill_length_mm	decimal lengths in millimetres	2
bill_depth_mm	decimal depths in millimetres	2
flipper_length_mm	whole-number lengths in millimetres	2
body_mass_g	whole-number masses in grams	2
sex	female, male, NA	11
year	2007, 2008, 2009	0

Part 4 — The Scaffold

Sort every column before you summarize it, because the type and level decide which summaries make sense.

Student scaffold table 1

Column	What it records	Categorical or quantitative	Discrete or continuous (quantitative only)	Level of measurement	Missing (NA) count	Reason in one line
species
island
bill_length_mm
bill_depth_mm
flipper_length_mm
body_mass_g
sex
year

Count the birds in each species so you can check that the parts add back to the whole file.

Student scaffold table 2

Species	Count from COUNTIF
Adelie
Chinstrap
Gentoo
Total of the three

Part 5 — Analysis Questions

1. Using your missing-value counts, how many penguins have a recorded body mass, and how many have a recorded sex? Show each subtraction from 344.
2. Which columns have missing values, and what type of variable is each of them? Which columns have none?
3. Explain why year is interval rather than ratio, and why an average of the island column would mean nothing.
4. For the question 'how heavy are the penguins of the Palmer Archipelago?', is the field log a population or a sample? Name the parameter the question is about and the statistic the file lets you compute.
5. Choose one and answer it in 3–4 sentences. (a) The field log is an observational study: name one question about penguins it cannot answer, and describe a study that could. (b) The researchers logged the birds they found: name one way the birds they found might differ from the birds they missed, and say how that could mislead a conclusion.

Part 6 — AI-Critique Moment

Choose one equal-credit critique route. Route A: ask an approved AI tool to critique your completed work, then check its response against your own evidence. Route B is the no-AI equivalent printed below.

Approved tools: Claude, Gemini, ChatGPT, Grok, Microsoft Copilot.

Prompt to paste:

I've pasted my completed Week 1 Data Lab tables above. The Palmer penguins file has 344 rows and 8 columns: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, sex, and year. Please do five things. First, classify each column as categorical or quantitative and give its level of measurement. Second, say how many values are missing in each column. Third, describe the shape of the body mass values. Fourth, say whether these data show that living on Biscoe causes penguins to be heavier. Fifth, as a stretch question for later in the course, say how sure we can be about the average body mass of all penguins in the archipelago, and what the chance is that exactly one of two penguins picked at random from the file is a Gentoo.

Test the response for these likely failures:

• Chatbots often state counts they never checked, such as a round number of rows, no missing values at all, or missing counts that differ from the file.
• Chatbots often slide from a pattern to a cause, for example saying that Biscoe's conditions make its penguins heavier because the heavy Gentoo penguins all live there.
• Asked about shape, chatbots often name the skew by where most values pile up, calling body mass left-skewed because most birds are heavy.
• Asked how sure we can be, chatbots often invent an interval and say there is a 95% chance the true average is inside it, or that 95% of penguins fall inside it.
• On the stretch chance question, chatbots often multiply the chance of a Gentoo by the chance of a non-Gentoo once and stop, forgetting that the one Gentoo could be the first pick or the second.

Write 3–5 sentences naming one thing the chatbot got right and at least two places it went wrong or overreached, quoting its words and correcting each with evidence from your own tables or the field log.

Equivalent route (no AI required)

No AI account is required for this lab. Instead of the chatbot, critique this flawed analysis, printed here in full: "The penguin file has 350 rows and no missing values. Year is a ratio variable, so the year 2008 is twice as late as the year 1004. Body mass is left-skewed, because most of the penguins are heavy. Living on Biscoe makes penguins heavier, since the heaviest species lives there. We can be 95% sure that 95% of all penguins weigh exactly 4,201.75 g. The chance that exactly one of two random penguins is a Gentoo is the Gentoo chance times the non-Gentoo chance, and nothing more." Mark it up: quote each flawed sentence, name what is wrong (an invented statistic, a flipped skew direction, cause read from association, a misread confidence statement, or a dropped number of ways), cite the evidence from your tables or the field log, and write a corrected sentence beneath it; for the last two sentences, say what the course will check later. This route assesses the same objective (Objective 1 — distinguish populations from samples and parameters from statistics, classify variables by type and level of measurement, and identify appropriate sampling methods and study designs, including what each can and cannot conclude) as the chatbot critique, takes comparable effort (about 30 minutes and 200–300 words), is scored by the same scoring criteria for the same credit, and its own submission artifact is the marked-up flawed analysis, posted in place of the chatbot share link and report.

Part 7 — What to Submit

• Both completed scaffold tables
• Written answers to all five analysis questions
• The chatbot critique report with its share link, or the equivalent route's marked-up flawed analysis

Deliverable:

A short Data Lab report on the real Palmer penguins field log: two completed tables (the eight columns sorted by type, level, and missing count, and the species counts that add back to 344), written answers to five analysis questions, and a critique of a chatbot's analysis of the same file, or, on the equivalent route, a marked-up correction of the printed flawed analysis. Submit in any accessible file type or as a link; a recorded response may be submitted as audio or video with a transcript, or as text. Choose a written, recorded, or visual form for your answers; every form is graded by the same scoring criteria.

Rubric

The rubric below shows how each submitted part is evaluated; bands are full, partial, or none.

Workshop evaluation criteria

Criterion	Full	Partial	None
Getting and counting the data	The file is opened or the fallback model data is used, the row count of 344 and every column's missing count are recorded, and the three species counts add back to 344.	The data are opened and counted, but one or two counts are missing or do not match the file, or the species counts are not checked against the total.	No counts are recorded, or the counts are invented rather than computed.
Sorting every variable by type and level	All eight columns are classified as categorical or quantitative, quantitative columns as discrete or continuous, and each by a correct level of measurement with a sound one-line reason.	Most columns are classified correctly, but some reasons are missing or one or two levels are wrong, such as year called ratio.	The classification is missing or mostly incorrect.
Interpreting the field log as a study	Analysis questions 1 to 4 are answered accurately: the two subtractions from 344, the columns with missing values, why year is interval and an average island is meaningless, and the sample, parameter, and statistic for the weight question.	The questions are attempted, but one or two answers blur sample and population or parameter and statistic, or leave out a reason.	The analysis questions are missing or do not address the field log.
Critique of the chatbot or of the printed flawed analysis	On either route, at least two errors are quoted and corrected with evidence from the student's own tables or the field log, and one correct point is acknowledged; both routes earn this band equally.	On either route, errors are named but corrected without evidence, or only one error is caught.	No critique appears, or pasted output is submitted without any checking.
Reflection on what the study can show	The chosen reflection option is answered in 3–4 sentences that connect the field log's design to a specific limit on its conclusions.	The reflection is answered but stays general or does not name a specific limit.	No reflection is submitted.

Quality Gate

• Every count in the tables comes from your own formulas or the fallback model data, never from memory or a chatbot.
• The species counts add back to 344, and each missing-value count matches a formula you can show.
• Every column has a type, a level of measurement, and a one-line reason.
• No statistic, count, or source is invented anywhere in the submission.
• The critique quotes the chatbot or the flawed analysis and corrects at least two errors with evidence from your tables.