Bigfoot Data We Ignore: Why “Nothing” Could Be the Key to Discovering Sasquatch
What if the secret to finding Bigfoot isn’t new technology, but data about nothing? In this post, I explore the scientific value of null data, the unreported “zeros” from field investigations where no sightings occur. Learn how documenting absences can transform Bigfoot research into a true ecological science using presence-absence data, occupancy models, and detection probabilities.
Video Transcript
The Power of Nothing: Why Null Data Matters in Bigfoot Research
Is the secret to finding Bigfoot… nothing?
It might sound strange, but both Bigfoot research and a lot of wildlife science are missing something — and it’s not better cameras or equipment. It’s data… but not the kind you might think.
What we’re missing is null data — the data of literally nothing.
The research trips and investigations where nothing was observed. No sightings, no sounds, no footprints. And yet, these zeros might be some of the most powerful numbers in Bigfoot data science.
Who I Am
Hi, I’m Terrestrial — a naturalist, ex-NASA researcher, and the creator of the Sasquatch Data Project.
I love math, science, statistics, AI, and (of course) Bigfoot. 🌿✨
If those are your jam too, make sure to follow along — we’re bringing scientific rigor into one of the world’s most fascinating mysteries.
What Is Null Data?
When someone goes out looking for a species — say, a heron, a bobcat, or a Sasquatch — and comes back with nothing, that’s null data.
Examples:
A birder spends a day at a pond and sees nothing.
A camera trap runs for weeks and captures no animals.
A hiker reports no unusual sounds or movement.
This kind of information is rarely reported, because people think: “Nothing happened — why would that be useful?”
But from a biological, ecological, and statistical perspective, these blanks are gold.
Why Absences Matter
Let’s step outside the Bigfoot world for a second.
Imagine you’re a biologist studying a rare frog species. You map where frogs are found — that’s helpful, but it’s not the full story.
Without recording where frogs weren’t found, you can’t:
Distinguish between true absences and places no one looked
Compare effort vs. results
Estimate the true probability of finding the species
In ecology, survey teams log every site they visit — even the ones with no detections. That’s how you build a complete dataset, not just a highlight reel.
Presence-Only vs. Presence-Absence Data
Most Sasquatch reports are presence-only: someone saw or heard something and reported it. This kind of data is useful (you can run models like MaxEnt), but it can’t tell you where Sasquatch isn’t.
Presence-absence data, on the other hand, records both:
Where the species was observed, and
Where it wasn’t.
With that kind of data, we can use tools like logistic regression and occupancy modeling, giving us a far clearer picture of where a species truly occurs.
A Selfie Analogy
Think of social media.
People take selfies at famous landmarks — and if you mapped those, you’d see hotspots. But that map wouldn’t show where people aren’t taking selfies.
Selfie posts = presence-only data
Everyone else = presence-absence data
To truly understand patterns, you need both.
The Problem of Imperfect Detection
Even if a species is present, you might not detect it.
Maybe a bobcat walks just behind your trail camera. It’s there — you just didn’t catch it.
That’s called imperfect detection, and it means “no observation” ≠ “absence.”
In Sasquatch research, this could happen if a creature moves behind your camera or observes you unseen. But if we collect enough null data, we can start to estimate detection probabilities — the odds of noticing something that’s actually there.
The basic equation is:
Probability of detection = ψ × p
where ψ = probability the species is present,
and p = probability of detecting it if it is.
If you only have presence-only data, you can’t separate those two variables — but null data lets you.
The Denominator Problem
Right now, Sasquatch data is all numerator — the reports themselves.
But without the denominator (how many times people went out and found nothing), we can’t calculate meaningful rates.
For instance:
Two reports from Yosemite (visited by millions) are not equivalent to two reports from remote wilderness.
Null data gives us context — how many trips, how many nights, how many investigations resulted in nothing. This helps control for effort, just like in birding platforms such as eBird, where observers always log how long they searched and how many people were present.
Handling Absence Carefully
A recorded absence doesn’t always mean true absence — but ecologists handle this by repeated surveys.
If a site is checked five or six times with no detections, the probability of a true absence rises.
So, for Sasquatch fieldwork:
Document every time nothing happens.
It’s not glamorous, but it’s statistically powerful.
What We Can Learn from Null Data
Collecting null data could:
Distinguish real Sasquatch hotspots from human hotspots
Estimate encounter probabilities (e.g., “1 encounter in 100 visits = 1% chance”)
Enable occupancy models, effort covariates, and other mainstream ecological methods
Reveal long-term trends — are reports increasing, decreasing, or stable?
In short, null data lets us move from speculation to testable hypotheses — and that’s what makes it science.
The Takeaway
Presence-only data shows us patterns.
Null data turns those patterns into something testable.
It gives us denominators, detection probabilities, and the ability to place Sasquatch research on the same scientific footing as mainstream biology — and that’s pretty incredible.
So if you’re in the field:
Start recording every time nothing happens. Those zeros matter. 🌿
Stay Connected
If you enjoyed this topic, check out my other work with the Sasquatch Data Project — where we bring science, structure, and statistics into the Bigfoot conversation.
Follow me on:
YouTube, Instagram, Facebook, TikTok: @SasquatchData
Stay curious, stay data-driven, and as always — stay rad.
