Example expressions (click to use):

You can also share expressions by adding ?expr= to the URL. For example: ?expr=~%20100

# about 100
~ 100

~ 1 in 100

# also works:
about 10 / roughly 50 * ~ 1 in 10

# triangular: mode 30, min 20, max 60
~ 20 / 30 \ 60

# sample from a list (with replacement)
~ [1,2,2,3,3,3]

# find the best fit to centile values
# (5th, 50th and 95th by default)
~ 1 | 10 | 100

# fit to specific centiles
~ 25th@10 | 50th@12 | 75th@17

# with constraints
~ 1 | 10 | 100 {>0, <120}

# Examples of all the different distributions

# Assumed

~ 10
~ 10 {count}
~ 10 {norm}

~ 1/10\20
~ 1 in 10

# FITTED by quantile values

# Normal distribution
~ 10 | 20 | 30

# log normal (found to be the best fit)
~ 0 | 5 | 30

# log normal (forced)
~ 0 | 2 | 10 {lognorm}

# Forced gamma
~ 0 | 5 | 100

# Weibull distribution
~ 5th@4 | 50th@10 | 95th@14

# t dist
~ 5th@10 | 25th@80 | 50th@100 | 75th@120 | 95th@190

# the flake equation
# https://xkcd.com/718/

Wp = ~ 8 * 10 ^ 9   # world pop.

# fraction people who imagine aliens to feel special
Cr  = ~ 1 in 10000

# fraction people who mistake natural phenom for aliens
Mx = ~ 1 in 10000

# p(tell someone)
Tk = ~ 1 in 50

F0 = ~ 5 # number people they tell
F1 = ~ 5 # n who retell

# prob. details revised
Dt = ~ 99 in 100

# fraction with means to publicise
Au = ~ 1  in 100

number_of_credible_alien_sightings = Wp * (Cr + Mx) * Tk * F0 * F1 * Dt * Au

Wp = ~ 7.9e9 to 8.1e9   # world pop.
Sneezes_per_day = ~ .2 |2 | 20 {>0, <100}
Seconds_per_day = 24*60*60
proportion_day_awake = .75
sneezes_per_second = (Wp * Sneezes_per_day) / Seconds_per_day* proportion_day_awake

# are there more than 1m per second?
sneezes_per_second > 1e6

Wp = ~ 7.9e9 to 8.1e9   # world pop.
Awake = ~ 7 in 10
Outdoors = ~ 2 in 10
Happy = ~ 1 in 10
CanWhistle = ~ 1 in 2
DoWhistle = ~ 1 in 100

number_people_currently_whistling = Wp * Awake * Outdoors * Happy * CanWhistle * DoWhistle

# holiday budget

food_cost = ~20 / 25 \ 70  # Daily £, pp
flight = ~200
hotel = ~80 / 120 \ 180
days = ~5 {>3}
total_cost = (food_cost * days * 2) + flight + (hotel * days)
total_cost < 1500

# Simplified drug development decision model

# Phase 2 success probability (Wong et al., 2019)
phase2_success = ~28 in 100

# Development cost in millions
development_cost = ~80 / 150 \ 300

# Market potential if successful
market_value = ~ 25th@300 | 50th@500 | 90th@1200

pipeline = ~8

potential_return_millions = pipeline * phase2_success * (market_value - development_cost)

# Monetary cost of climate damage caused by a typical
# shorthaul flight, using a social cost of carbon approach.

# Round trip distance, including overhead
typical_km = ~ 1000 ... 3000

# industry figures
kg_co2_per_km = ~ 0.22 / 0.25 \ 0.30

# for radiative forcing,
# DEFRA/IPCC suggest multiplier of 1.7 to 2.0
rf_multiplier = ~ 1.5 to 2

# social cost of carbon;
# convert £/tonne to £/kg
scc = (~ 50 to 100) / 1000

# totals
emissions = typical_km * kg_co2_per_km
total_cost_gbp = emissions * rf_multiplier * scc

# savings from reducing burnout in employees

burnout_rate = ~ .01 / .02 \ .05  # annual %
loss_per_person = ~ 10@500 | 50@1000  | 95@5000 {>250}
N = 8000 # n employees
tx_reduces_burnout_by = ~ .0 to .02

current_costs  = burnout_rate * loss_per_person * N
new_costs = (burnout_rate - tx_reduces_burnout_by) * loss_per_person * N

savings_from_tx = current_costs - new_costs