Plenty of people can nail the price of a convenience-store soda to the last dime, then freeze when you ask, "How much is an ounce of gold?" Both are just prices, so why the freeze? Because the brain handles prices by memory, not by math. It is sharp where prices are familiar and simply stalls where they are not. The interesting part: that stall is not random from person to person — it goes wrong in almost the same patterns. Fifty years of psychology experiments have names for those patterns. Let us walk through them.

A roulette number drags your answer around — anchoring

In 1974, Tversky and Kahneman ran a slightly absurd experiment. They had people spin a roulette wheel (secretly rigged to stop on 10 or 65), then asked, "What percentage of UN members are African countries?" The people who landed on 10 answered 25% on average; those who landed on 65 said 45%. Everyone knew the wheel had nothing to do with Africa, and they still got pulled by the number they had just seen.

With prices it bites even harder. In 1987, Northcraft and Neale showed 47 real-estate professionals the same house, changing only the listed price — $149,900 for one group, $119,900 for the other. Their estimated values came out about $13,200 apart, roughly 11%. Twenty years of experience did not free them from the anchor. Scarier still is Englich's 2006 study: 87 German judges read a case file, rolled dice, then set a sentence. Judges who rolled a 3 gave 5.3 months on average; those who rolled a 9 gave 7.8. The dice swung the judges by 47%.

"But if you know about it, you can dodge it, right?" Wilson tested exactly that in 1996. Even with a warning, the effect shrank by only 27%. Seventy percent stayed. This is not something you block by knowing — it runs automatically the moment you see the number.

The brain feels a million and a billion as almost the same — scale insensitivity

100 and 1,000 feel clearly different. But $1,000,000 and $1,000,000,000? Both register as "a huge amount," even though one is 1,000 times the other. Hsee's 2003 experiment is clean: place "a policy that saves 1,000 lives" next to "a policy that saves 100,000 lives" and the gap is obvious; evaluate them separately and support is nearly identical. A 100x gap vanishes when you see it alone.

Brain scans back this up. Per Dehaene (1997), the parietal region that estimates large numbers responds to ratio, not absolute size. The neural response comparing 10 vs. 20 is statistically the same as comparing 1,000,000 vs. 2,000,000. That is why, in PriceGuess's Higher or Lower, people pin NVIDIA ($3.3T) at "maybe 2x" Tesla ($800B) when it is really 4x. Both get buried in the single bin "trillions," and the ratio inside it never lands.

Ask "average CEO pay" and Musk shows up first — availability

We judge how often something happens by how easily it comes to mind. Plane crashes feel more dangerous than car crashes because of coverage, not statistics. With prices, this pushes averages upward. Ask about "average CEO pay" and Musk's $52B option package or Tim Cook's $99M surfaces first — even though the median S&P 500 CEO makes $16.3M. Schwarz's 1991 study adds a twist: people asked to recall 6 confident moments rated themselves as more confident than people asked to recall 12. Twelve was hard to dredge up, so they read that difficulty as "I guess I'm not that confident." The feeling of "hard to recall" becomes the answer itself.

Yesterday's price becomes "normal" — recency

In Murdock's 1962 recall experiment, people remembered the first and last items in a list far better than the middle. Prices work the same way. Yesterday's number sits heavier than last month's and quietly becomes the bar for "normal." Show someone Bitcoin at $73,750 in March 2024, ask in May about $58,000, and they keep guessing near 73K. The standout case is Black Monday, 1987. The week after the Dow fell 22.6% in a single day, a Yale survey found 87% of individual investors expecting another crash soon. Instead the market recovered 23% over the next 12 months and rose sixfold over the decade. One shock bent every expectation that followed.

Prices are remembered as stories, not numbers — narrative

Robert Shiller, the 2013 Nobel laureate, argued in a 2017 lecture that roughly half of economic "data" spreads as stories about why prices moved. The trouble is that the stories usually show only one side.

• "Gold is a safe haven, so it steadily rises" — it went sideways for six years (2013–2018) and, inflation-adjusted, fell about 80% from 1980 to 2000.
• "Real estate always goes up" — Japan's average nominal home price in 2024 still sits around 60% of its 1991 peak.
• "Tech is the future, so it's worth it" — in 2002, Cisco dropped 80% and Amazon 95%, and plenty of dot-coms never came back at all.

The real trap in a story is not "wrong information" but "selected information." Only the survivors' stories stick around; the ones that died drop out of the sample. Which is exactly the next bias.

Judging from the planes that came back — survivorship bias

In WWII, the military looked at bullet holes on returning bombers and said, "Reinforce where they're hit most." Mathematician Abraham Wald said the opposite: "The untouched spots are the real weak points — planes hit there didn't make it back." The missing data was the answer. Stocks are the same. The oft-quoted "S&P 500 returned about 10% a year for a century" is an average of the survivors only; the −100% of companies that went bankrupt and delisted is left out. Brown, Goetzmann, and Ross (1995) estimated this illusion inflates long-run U.S. returns by about 2.5 percentage points. Of the 457 dot-com IPOs in 1999, 60% were delisted within five years — but we remember only the Amazons and eBays.

$1,000 in 2015 isn't the same unit as $1,000 in 2026 — units and timing

The most common slip, and the least noticed. By the U.S. BLS calculator, $1,000 in January 2015 is about $1,340 as of May 2026 — 34% of its real value gone in 11 years. Compare the nominal numbers and it looks flat; in real terms it's −34%. Exchange rates do the same. One dollar bought 580 won in 1980 and about 1,400 won in 2026, which looks like a much weaker won — but over that span U.S. prices rose about 3.7x and Korean prices about 5.8x. In real terms the weakening is far smaller, and in some stretches the won actually strengthened. "Gangnam apartment prices 30 years ago" look cheap for the same reason; fold in 3.5x prices and 7x average wages and the real burden may have been heavier back then.

Being smart doesn't make you immune

"I'm clever, so I'm fine" has no evidence behind it. Stanovich and West (2008) compared high- and low-SAT scorers on bias and found almost no gap on things like anchoring and framing. So the takeaway isn't "get smarter" — it's "build guardrails": answer in ranges, check outside data, listen to other views.

How to actually check yourself

• Start with a range, not one number. A "low–high" spread beats a single average for accuracy.
• Pin the unit and date first. Make "in what year's money?" an automatic question; adjust for inflation before comparing.
• Wait a beat after seeing a listed price. Wilson's data says it only cuts the anchor by 27%, but that beats zero.
• Do millions/billions/trillions as ratios. Lock in the multiple first — "NVIDIA is 4x Tesla."
• Deliberately ignore the latest headline. Pull up the 1-year and 5-year average, not just this week's.
• Count the cases that disappeared. Recall something like "60% of 1999's 457 dot-com IPOs were delisted."
• Check the answer immediately when you're wrong. Roediger and Karpicke (2006) found this kind of instant-feedback practice builds long-term memory about 50% stronger than plain review.

Getting it right isn't the goal

The real goal of price estimation isn't a high hit rate — it's knowing exactly where your intuition goes off. Knowing the biases won't block all of them (about 27%, by Wilson's number). But it buys you the room to pause once before a big call. PriceGuess is a tool for drilling that pause until it's automatic. Start light with Higher or Lower or today's price.