Works with Claude, ChatGPT, Gemini, or your own local model: paste this prompt and your AI will co-facilitate this exercise with you.

Help me prepare and run the exercise below. First ask for my participant count, time, setting, access needs and intended result. Check these against the stated limits. Use the supplied rules and materials. Explain any proposed changes before using them, and label them as adaptations. Do not invent source claims or require personal disclosure. Give me one step at a time when I say start. I manage the people, physical activity and clock; do not claim to observe the room. Ask me what happened before choosing a next step.

Reference revision: e24d5540146ef37d85e2cb539ab07336e32f04e13d9661590025ef16ded70e09

# Guess the Number

Participants estimate the count of items in a clear jar across three iterative rounds, viewing pooled results between rounds. The facilitator calculates group averages to show how iterative group estimation compares to individual guesses.

**Time:** 25 minutes. Adapted to a tight 25-minute run from the 30 to 40 minute source range to fit practical workshop schedules.
**People:** 4 to 24. Individual estimation with whole-room visible aggregation

## Choose this exercise

**Purpose:** Demonstrate how iterative estimation rounds and pooled feedback change estimate distribution and group accuracy.
**Use it when:** Use during forecasting, planning, or decision-making workshops to introduce iterative estimation concepts like the Delphi method before tackling real work estimates.
**Skip it when:** Skip when the meeting needs immediate decisions on known operational facts or when participants lack time for three rounds of data entry.

## Materials and tools

- One transparent jar filled with a known count of identical small items (such as beans, buttons, or marbles)
- Three index cards or small slips of paper per participant
- One pen or pencil per participant
- Whiteboard or flip chart with marker
- Calculator or digital spreadsheet to compute round averages

## Before participants arrive

1. Count the items beforehand and record the true number out of sight.
2. Place the filled transparent jar on a central table visible from all seats.
3. Draw three summary columns on the whiteboard labeled Round 1, Round 2, and Round 3, each with rows for Low, High, and Average.

## Say this to open

Look at the jar on the table. Do not touch or shake it. Each person will estimate how many items are inside. We will do this across three quiet rounds. Between rounds, you will see the group range and average. Write only your number on your slip.

## How to run it

### 1. Submit initial independent guesses (5 minutes)

Distribute one slip to each person. Participants inspect the jar visually and write their initial estimate on their slip without conferring. Collect all slips. Calculate the lowest number, highest number, and mathematical average. Write these three summary figures on the board under Round 1.

### 2. Review group range and revise (5 minutes)

Direct attention to the Round 1 summary numbers on the board. Hand out a second slip to each person. Ask participants to reflect on the group spread and write a revised estimate on their second slip. Collect the slips, calculate the new low, high, and average, and write them under Round 2.

### 3. Make final estimates and reveal truth (7 minutes)

Distribute the third slip. Participants write their final estimate based on the trend. Collect slips, compute the Round 3 low, high, and average, and post them. Announce the true count. Circle the group average that came closest to the true count.

### 4. Debrief the estimation process (8 minutes)

Conduct a whole-room debrief using the posted numbers. Take 3 to 4 quick observations from volunteers about how their individual estimates changed and how the group average performed against individual outliers.


## Debrief questions

- How did seeing the group range and average in Round 1 influence your estimate in Round 2?
- Did the group average get closer to the actual number across rounds, or did it anchor on early estimates?
- Where in our actual project work could an iterative estimating approach prevent single-person guessing?

**Finished output:** A completed three-round summary chart showing high, low, and average estimates alongside the true item count.

## Remote

The facilitator displays high-resolution photos of the jar from multiple angles (top, side with a standard ruler for scale) and holds the jar up to the webcam. Participants submit estimates via a private digital form or direct chat to the facilitator. The facilitator pastes the data into a spreadsheet that auto-calculates min, max, and mean, then shares the screen showing the summary chart.

## Hybrid

Remote participants view high-resolution photos with a scale reference on a shared board and see the live jar over video. In-person participants write on slips while remote participants submit via a direct message to a co-facilitator. The co-facilitator logs all entries into a single shared spreadsheet displayed on the room screen and shared window simultaneously.

## Access and participation choices

- Provide a standard reference scale such as a ruler resting beside the jar to assist visual estimation.
- Participants may pass on submitting a number or observe the calculation rounds without penalty.

## If the session gets stuck

**A participant tries to handle, open, or shake the jar.**

Remind the group before Round 1 starts that the jar remains on the table untouched so everyone has identical visual information.

**Manual calculation of the average takes too long and slows down the room.**

Use a pre-formatted digital spreadsheet where you only type the numbers, allowing the formula to output low, high, and average instantly.


## Terms used here

- Delphi method: A structured forecasting technique where participants estimate independently, review anonymous group results, and revise their estimates over multiple rounds.

## Sources and adaptation

Observed in Participatory Methods (participatorymethods.com) as an experiential teaching exercise for the Delphi estimation method.

Retained the three-round iterative jar-estimation mechanic from Participatory Methods. Streamlined the process to 25 minutes by structuring calculation pauses with index cards or a pre-set spreadsheet. Clarified remote scale requirements and added standard access options.

[Participatory Methods](https://participatorymethods.com/activities/guess-the-number/)

Current run sheet: https://unitedwetransform.com/exercises/guess-the-number/