Two prior posts worked through the statistics of the SB 6346 sign-in data. In the first I established the methodology and the finding. After applying a birthday-corrected collision test to separate organic participation from anomalous windows, roughly 90,000 legitimate CON participants remain against roughly 9,100 legitimate PRO participants. In the second I addressed the legislature’s claim that duplicate names make the dataset unreliable. The finding runs the other way. A genuine sample drawn from a real community produces name collisions at a predictable rate. People share surnames, people hit submit twice, households have two people with the same name. The PRO overnight batch produced zero collisions across 934 draws, where the statistical minimum expected is around 30. The anomaly is suspicious precisely because it has too few duplicates, not too many. Real participation is messy. This was not.

This post is not about those results. It is about what legislators said about them at a February 24 media availability, and whether their positions are statistically defensible.

They are not.

The Math Problem With “Not Helping Us Make Decisions”

“It’s not like we are making decisions not to pass a bill because of a sign in… they’re not really helping us make decisions in terms of amendments to bills or whether to pass it out of committee or not. We rely on people who actually come and testify in person.”

— Senator Manka Dhingra, February 24 media availability

That is a statistical claim. It asserts that the sign-in data has no decision-relevant information. For that to be true, one of two things must hold. Either the signal is too noisy to be meaningful, or legislators have better information that makes it redundant.

Neither holds.

The 10:1 ratio across 90,000 legitimate responses is not ambiguous. The margin of error at that sample size is roughly a third of a percentage point. The ratio does not wobble under any standard statistical treatment. Even applying the most aggressive self-selection correction anyone has proposed, assuming CON participants are twice as motivated to engage as PRO participants, the adjusted ratio is still 5:1. The signal does not disappear. Calling it noise is not a statistical judgment. It is a refusal to do the math.

As for better information, what would that be? Testimony at a two-hour hearing. Phone calls. Letters. The intuitions of members who have held their seats for multiple cycles. None of those are more statistically rigorous than 90,000 data points. Most are orders of magnitude less rigorous. If a senator’s read of the room outweighs a dataset this large at a ratio this clear, that is not superior methodology. That is substituting anecdote for evidence.

Dhingra’s preferred alternative, people who show up in person, has its own problem. The photo below is from the February 6 Senate hearing. The room is full of people in matching purple shirts and teal sashes. That is coordinated turnout, organized in advance, by people with the resources and flexibility to get to Olympia on a weekday. It is the physical equivalent of a sign-in campaign, except it requires taking a day off work and driving to the state capitol.

That standard also systematically excludes the people most affected by legislation. A small business owner in Spokane worried about a new tax on their income cannot easily testify on a Wednesday. A nurse working a shift cannot. A retired teacher in Yakima cannot. The sign-in system exists precisely because geographic and economic barriers make in-person participation inaccessible to most Washingtonians. Dismissing sign-ins in favor of in-person testimony is not a quality upgrade. It is a substitution of one self-selected sample for a smaller, more organizationally filtered one.

What Statistically Relevant Engagement Actually Looks Like

A standard poll commissioned to gauge public opinion on a major policy question uses around 1,000 respondents. That produces a margin of error of roughly 3.1% at 95% confidence. Those numbers drive legislation, inform campaign strategy, and get cited on the floor. Nobody demands methodology disclosure before a senator cites a Crosscut poll. That is simply the accepted evidentiary standard for constituent sentiment.

The sign-in dataset, after deduplication, contains roughly 90,000 legitimate CON responses. While strict margin of error calculations require randomized polling rather than opt-in data, the mathematical gravity at this scale is inescapable: a random sample of this size would carry a margin of error of approximately 0.33%. This dataset is ninety times larger than what legislators already treat as a reliable signal, with precision ten times tighter.

Washington has approximately 5.5 million registered voters. Ninety thousand responses represents roughly 1.6% of that population engaging with a single bill in committee. In political science research on constituent contact, engagement rates on individual pieces of legislation are typically measured in fractions of a percent. At 1.6%, this dataset is not a rounding error above that baseline. The prior record for sign-ins on any Washington bill was reportedly around 45,000, itself considered extraordinary. This dataset doubled it, and the legislative website crashed under the volume because nothing in the system’s design anticipated engagement at this scale.

The infrastructure of participation failed because the signal exceeded its design limits. That is not a data quality problem. That is evidence of something real happening in the electorate.

To put 90,000 in electoral terms: Washington has 49 legislative districts. Distributed statewide, that averages roughly 1,800 CON sign-ins per district. The 2024 state Senate race in the 10th district was decided by 153 votes. The House race in the 17th district was decided by fewer than 200. Several competitive seats turned on margins smaller than the number of people in those districts who showed up to oppose this bill. Legislators are not dismissing a fringe signal. They are dismissing a constituency that is, in several of their districts, larger than their margin of victory.

Consider how the same legislators would respond to a poll of 1,000 Washingtonians showing 10:1 opposition to a bill. That finding would be treated as dispositive. It would be cited in floor speeches, appear in press releases, and be described as a clear signal of constituent sentiment. This dataset shows the same ratio at ninety times the sample size, with a margin of error ten times tighter, with an audit trail, with a reproducible methodology, and after removing anomalous windows on both sides.

The legislators who called it noise do not apply that standard to anything else they use.

You Don’t Need to Read the Bill

“I don’t think everyone who’s signing in in support or opposition is actually reading the bill. So I think you got to take it for what it’s worth.”

— Senator Yasmin Trudeau, February 24 media availability

For a technical bill where the title might mislead, that would be a legitimate point. SB 6346 is not that kind of bill. Washington has not had an income tax in nearly a century. Voters have rejected it ten times. For most constituents signing in CON, reading the bill is beside the point. They already know where they stand. The question SB 6346 raises for them is not what the rate structure looks like. It is whether Washington should have an income tax at all, and on that question they have a consistent ninety-year answer. Beyond that settled position, the architects of this legislation documented their strategy in writing years before the bill was introduced.

In April 2018, Senator Jamie Pedersen sent an email to a former Democratic legislator explaining the real value of passing a capital gains tax. The major use of revenue, he wrote, was secondary. The more important benefit was on the legal side. Passing a capital gains tax would give the Supreme Court the opportunity to revisit its decisions that income is property, and would “make it possible to enact a progressive income tax with a simple majority vote.” Those emails were obtained through public records and published by the Washington Policy Center, which also documented the three-step sequence Pedersen described. Pass the capital gains tax to break the legal seal. Pass a millionaires tax to build the administrative infrastructure. Then lower the threshold to capture the middle class.

The capital gains excise passed in 2021. Pedersen also promised the revenue would reduce property and sales taxes. The state collected $1.8 billion in capital gains revenue from 2022 to 2024. Not a dollar went to reducing property or sales taxes. New spending absorbed everything. The Supreme Court upheld the excise in Quinn v. State in 2023, doing precisely what Pedersen predicted. A surcharge was added in 2025. SB 6346 arrived in 2026 as the simple majority vote Pedersen described eight years earlier.

A constituent who signs in CON without reading SB 6346 but who knows this history is not pattern-matching by instinct. They are responding accurately to a documented legislative strategy, now in its final stage, by an architect who wrote down the plan. Trudeau’s concern assumes the sign-in reflects ignorance. The record complicates that assumption.

The federal income tax was introduced in 1913 as a temporary measure with a top rate of 7% on incomes above $500,000. It has been neither temporary nor limited since. A constituent who has watched Washington’s capital gains excise follow the same arc, introduced with tax relief promises that were never kept and expanded within four years, is not being paranoid. They are reading the pattern correctly.

A constituent signing in CON on this bill is not evaluating the mechanics of a 9.9% rate on income above one million dollars. They are evaluating a mechanism with a documented history and a stated long-term purpose. That is not noise. That is the signal working as designed.

The Participation Double Standard

“As a general rule, I always warn my members, you shouldn’t really pay attention to that kind of dialogue… maybe focus less on numbers and more on quality of engagement.”

— Speaker Laurie Jinkins, February 24 media availability

“Quality of engagement” implies that organized participation is lower quality than spontaneous participation. Applied consistently, that standard would disqualify most of what the same legislators celebrate as democratic infrastructure.

Get out the vote campaigns are organized, at scale, through forwarded links, text banking, social media mobilization, and door knocking. They systematically encourage people to act on issues they may not have independently researched. Nobody argues that a voter who was reminded to register by a campaign text is less legitimate than one who showed up spontaneously. Nobody demands that turnout in heavily canvassed precincts be discounted because the participation was encouraged rather than organic.

The asymmetry is hard to explain on principled grounds. Get out the vote efforts are explicitly designed to shape electoral outcomes, which directly determines who holds legislative power. Organized sign-in campaigns are designed to inform legislators of constituent sentiment on a specific bill, which Jinkins then warns her members not to pay attention to anyway. If one is legitimate democratic infrastructure and the other warrants skepticism, that distinction requires an explanation nobody has offered.

The Self-Selection Argument Does Not Save Them

The legitimate version of the dismissal is astroturfing risk. Organized campaigns can mobilize sign-ins that do not reflect organic sentiment. Two problems follow.

First, the statistical work already addresses it. The anomalies I flagged in those prior posts run against the PRO side, not CON. The CON signal carries the messy collision fingerprint consistent with real people. The organized manipulation concern, applied rigorously and symmetrically, strengthens the CON signal rather than undermining it.

Second, self-selection disqualifies nothing legislators already use. Every constituent signal they rely on is self-selected. Calls. Letters. Town hall attendance. Donations. None represent a random sample of the electorate. The sign-in system is being held to an evidentiary standard that almost no feedback mechanism in democratic practice has met, and that standard is applied to nothing else.

What makes the sign-in data different from those signals is not that it is less reliable. It is that it is more systematic. It produces a record. It is auditable. It generated enough volume to run statistical tests on. The methodology applied here would hold up in a peer-reviewed context. The “I talked to my constituents” alternative would not.

For the underlying sentiment to be actually close to even, CON participants would need to be systematically ten times more motivated to engage through this specific channel than PRO participants. That is not a bias correction. That is a complete reversal of the observed signal. No one has offered a mechanism that produces that result.

The legislators dismissing this data are not applying a rigorous evidentiary standard. They are applying a selective one.

The Broader Pattern

In Disdain or Design? I wrote about what happens to constituent input in Washington when institutional actors have decided on an outcome. The user interface of democracy still renders. The buttons are there. What that piece examined is whether the backend those buttons connect to has been rewired.

The sign-in dismissal is that pattern made unusually explicit. When lawmakers assert that sign-in anomalies damage the ‘democratic process,’ the irony is staggering. The legislature already removed the actual democratic process from this bill by attaching an emergency clause, deliberately blocking the public’s ability to challenge it via referendum. They pre-emptively silenced the electoral signal; now legislative leaders are simply stating on camera that the only constituent participation left is not helping them make decisions.

Washington voters have rejected income taxation ten times through the constitutional amendment process. The legislature is effectively circumventing the initiative process that most recently codified that preference. Dismissing the largest constituent response in state legislative history as something members should not pay attention to is not a data science position.

It is a tell about whose input actually shapes the outcome.

The Question That Deserves an Answer

Every signal legislators use to read constituent sentiment is self-selected. Calls. Letters. Town halls. Protests. Donations. Sign-ins are just self-selection at scale, with a paper trail rigorous enough to audit.

It is a perfectly reasonable position to argue that 90,000 highly motivated people clicking a web form do not flawlessly represent the entire state of Washington. But if the legislature genuinely wanted a higher-fidelity democratic signal, they would not have attached an emergency clause to explicitly bypass the voters. And they would not be ignoring a century of bipartisan ballot results where Washingtonians have rejected this exact policy ten separate times.

Legislators are free to make that choice, but voters deserve transparency about it, not a smokescreen of statistical skepticism that the data itself dismantles. When the numbers speak this clearly, ignoring them isn’t methodology; it’s a deliberate unplugging of democracy’s earpiece.

What This Is About

SB 6346 would create Washington’s first personal income tax in nearly a century. A 9.9% rate on income above $1 million, projected at $3.4 billion annually, it passed the Senate 27-22 on party lines and is now in the House. Washington voters have rejected income taxation ten times at the ballot over the last hundred years. This is the most contested piece of legislation the state has seen in a generation.

Washington’s legislature runs an online sign-in system for committee hearings. Anyone can go to the legislative website and register their position on a bill, pro or con, without testifying. Legislators see the totals. The system is designed to give ordinary people a voice even if they can’t show up in Olympia. On SB 6346, it may be the only meaningful voice many residents have: the legislature designated the bill an emergency measure, which prevents a voter referendum. There is no ballot option. For most Washington residents opposed to this bill, signing in here, or calling their representative directly, is the entire menu.

When those numbers get manipulated, the perception gets manipulated. On a bill this significant, in a state with a century of voter resistance to income taxes, that is not a minor data quality problem. It is a distortion of the democratic signal legislators and journalists are using to understand where the public actually stands.

Which is why getting the analysis right matters. And why it matters that GeekWire got it wrong.

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GeekWire reported Monday (Added 2/24/26: and apparently the Seattle Times too) that fraudulent sign-ins were used to inflate CON opposition to SB 6346. Named public officials confirmed their identities appeared without their consent. The framing was clear: the anti-tax side cheated.

The data tells a different story.

The story was built on analysis provided by Invest in Washington Now, a PRO-tax advocacy group that examined CON submissions and held a press conference. They reported a true incident, but failed to do the basic symmetric analysis needed to justify the narrative they attached to it.

I downloaded the full legislative sign-in export at 5:51 PM Pacific on February 23rd, 123,289 records, and ran the same tests on both positions. Here is what it shows.

The data confirms fraud. It does not confirm that fraud explains the opposition. Those are different claims, and the difference matters enormously on a bill this significant.

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Who Actually Showed Up

“Legitimate” here means a unique name that appears at least once during daytime hours (7 AM to 11 PM PT). The export does not verify identity.

CON: roughly 90,000 legitimate unique participants.

CON was underrepresented during overnight Pacific hours, which is exactly what you would expect from Washington residents who are asleep at 2 AM. By every geographic measure in the data, CON’s daytime participation pattern is consistent with Washington residents showing up. CON does have an overnight anomaly, roughly 2,800 names that appear only in overnight windows and never in five days of daytime sign-ins, which reduces the legitimate unique count to roughly 90,000. More on this below.

PRO: roughly 9,100 apparent legitimate unique participants.

The PRO side has 9,919 unique names across the full hearing. But between 1 and 5 AM Pacific on February 20th, 934 submissions arrived in a single five-hour overnight window (1:00–5:59 AM). That window accounts for about 8.4% of all PRO submissions across the entire hearing period. Of those 934 submissions, 807 were unique names. Set those aside, and you have roughly 9,100 apparent legitimate PRO participants.

That is a roughly 10-to-1 ratio (90,408 CON vs 9,112 PRO in the export, after removing overnight-only names as defined here). A margin like that on a contested tax bill during a legislature that removed opportunity for feedback other than this survey is not, on its own, statistically implausible.

Here is what the data actually shows. Both sides have anomalous overnight submissions that do not match the daytime participation signature. On the CON side, roughly 2,800 names, about 3% of their total, appear only overnight and never during five days of daytime sign-ins. On the PRO side, 807 names, about 8% of their total, appeared in a single five-hour overnight window. Both anomalies are real. But after removing them, roughly 90,000 legitimate CON participants remain against roughly 9,100 legitimate PRO participants. The fraud did not manufacture the opposition. The fraud, such as it is, was larger in proportional terms on the side that was already losing 10 to 1.

The story treated fraud as the explanation for the margin. The data shows the margin survived the fraud. On every test, the more anomalous signal sits on the PRO side, not the CON side.

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The Geography Test

The most straightforward test requires no statistics at all. Washington residents sleep on Pacific time. If you plot submissions by hour of day, genuine local participation should cluster during waking hours and drop off after midnight.

CON activity drops overnight, consistent with local participants sleeping on Pacific time.

PRO shows a pronounced spike on February 20th between 1 and 5 AM, running at close to 190 submissions per hour for five straight hours, while Washington residents were asleep and the CON side was quieter than usual. The spike is not a few night owls.

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The Community Test

Here is where it gets harder to explain away.

Across five full days of daytime sign-ins, we have an observable picture of who the PRO community actually is. Roughly 9,100 people engaged during normal waking hours. Five days is a long window. If you are a genuine PRO supporter, the probability that you appeared in that record at least once is high.

Name communities have statistical fingerprints. Any two groups drawn from the same population will share common names at a predictable rate, the same demographic mix, the same frequency of “James Kim” or “Sarah Johnson.” So even if the overnight submitters were entirely different individuals from the daytime crowd, you would still expect their names to collide with the daytime pool at a rate consistent with drawing from the same community. The longer the daytime window, the higher that rate gets.

You can test this directly. Draw 934 random names from the known PRO pool and ask how many appear somewhere in five days of daytime submissions. Across 10,000 simulations, the answer is about 86%.

The observed overnight overlap was 13.6%. Nearly nine out of ten overnight names had never appeared in five days of daytime PRO submissions.

Every one of the 10,000 simulations produced more overlap than the overnight batch did. The minimum was 82%.

The same test applied to CON tells a similar structural story. CON’s overnight names also show only 21–25% overlap with the daytime pool across five nights, against an expected 90–94%. Both positions have overnight participants who are largely absent from the five-day daytime record. The difference is in magnitude and concentration. CON’s anomaly spreads 2,800 names across five nights. PRO’s concentrates 807 names into a single five-hour overnight window. The PRO signal is sharper, but the underlying pattern of overnight names that don’t match the daytime community appears on both sides.

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The Name Collision Test

In any large population, names repeat. If you pull 934 people at random from Washington state, some of them will be named James Kim. Some will be named Sarah Johnson. That is not fraud, that is just how names work. The question is whether the names repeat at the rate you would expect given the size and demographic makeup of the community you are drawing from.

934 overnight PRO submissions produced zero repeated names. Not fewer than expected. Zero.

From a genuine community of roughly 9,100 PRO supporters, you would expect around 60 name collisions in a sample that size, just by chance. We ran 10,000 simulations drawing from the actual PRO participant pool. Every single one produced collisions. The lowest was around 30. The observed overnight batch produced none.

The most consistent explanation is that someone built a list and specifically made sure no name appeared twice. That is not what organic participation looks like. That is what a curated submission operation looks like.

The CON side shows the opposite pattern. Several overnight windows had more name repeats than expected, consistent with people resubmitting or households submitting together. Messy, in other words. The kind of messy that real participation produces.

934 submissions. 934 unique names. Zero repeats. That is the number that should be in the headline.

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On CAPTCHA

The form uses CAPTCHA verification. This comes up because some coverage implies it as a meaningful protection.

It is not, against this class of problem. CAPTCHA distinguishes automated bots from humans. It provides no protection against human click farms, which are operations that pay workers in other countries to solve CAPTCHAs manually and complete form submissions by hand. This is a commercial industry. Services are publicly listed, priced at $1 to $3 per 1,000 submissions.

The pattern observed on February 20th is most consistent with coordinated human submissions using a curated name list: overnight timing, near-zero overlap with the daytime community, and zero name collisions across 934 submissions. A click-farm-style mechanism is one plausible explanation. The public export cannot prove attribution without server-side logs.

At those rates, the 934 anomalous overnight PRO submissions represent a trivial cost against a bill projecting $3.4 billion in annual revenue.

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On the Audit Trail

The public data export does not include IP addresses. Whether internal server logs exist and whether they have been preserved is unknown. That is a question the AG’s investigation should answer before those logs age out.

Even with full IP logs, naive geolocation proves little. Click farm operations commonly route through VPNs, and an IP address alone does not establish geographic origin without infrastructure-level analysis of the autonomous system it belongs to. What you want to know is not which city the IP is registered to. You want to know whether it belongs to a residential ISP, a datacenter, or a known VPN provider range. Those are different findings with different implications.

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What This Means

Neither finding resolves cleanly without a real investigation. The named official impersonations on the CON side are real and the AG should pursue them. But confirming specific named victims is the easiest fraud to find because the victims can self-report. That is not a statistical audit, and it does not address what the data shows on the other side.

Both findings warrant investigation. The system made both possible.

There is a useful analogy here. Risk-Limiting Audits are the gold standard for post-election verification. The premise is that you do not need to check every ballot to establish confidence in the outcome. You need to bound the probability that the anomalies are large enough to change the result. Advocates of RLAs often argue, correctly, that statistical evidence is sufficient to certify an election without requiring individual identity verification for every voter.

That is precisely what this analysis does. It does not identify every fraudulent submission. It asks whether the fraud on either side was large enough to manufacture the margin. The answer is no. After removing every overnight anomaly on both sides, roughly 90,000 legitimate CON participants remain against roughly 9,100 legitimate PRO participants. If statistical sampling is rigorous enough to certify an election, it is rigorous enough to evaluate a legislative sign-in system.

The sign-in infrastructure was built for access. Low friction, no identity binding, no rate limiting that held against coordinated submission. I have written before about how Washington has accumulated individually defensible choices that collectively produce systems incapable of defending their own integrity. The legislature is now trying to adjudicate participation fraud on infrastructure that was never designed to be auditable.

The question that does not get asked in any of the coverage: why did Washington build a public participation system with no ability to verify, audit, or forensically reconstruct what happened, and what would it take to build one that can?

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Methodology

All analysis was run on the public CSV export of sign-in records for SB 6346, downloaded at 5:51 PM Pacific on February 23rd, 123,289 records total. Every test was applied symmetrically to both positions using the same parameters. The analysis does not attempt attribution. It bounds the probability of innocent explanation under stated assumptions.

Geographic analysis. Submissions were binned by Pacific hour. Each position’s hourly share was compared to that position’s overall base rate across the full hearing. CON activity drops overnight relative to daytime hours, consistent with participants sleeping on Pacific time. PRO showed a concentrated spike on February 20th between 1 and 5 AM, sustaining close to 190 submissions per hour across five consecutive hours. The 1 to 5 AM PT window corresponds to mid-day hours in parts of Asia and the Middle East.

Name overlap test. This test requires no statistical model and is not sensitive to assumptions about name distributions. For each overnight window with at least 20 submissions, the unique names were compared against that position’s daytime submissions (7 AM to 11 PM) across the full hearing. Overlap fraction equals names appearing in both sets divided by total overnight unique names.

To establish expected overlap, 10,000 random samples of size n were drawn without replacement from that position’s full-hearing participant pool, and the overlap fraction with the daytime set was computed for each draw. On February 20th, the PRO observed overlap of 13.6% fell below the minimum of all 10,000 simulations. The lowest simulated value was about 82%. CON overnight overlap ranged from 21–25% across five nights, against a bootstrap expectation of 90–94%, also below every simulation on every night. Both positions show overnight communities that are largely disconnected from their daytime pools.

Birthday-corrected collision analysis. Raw name duplication rates are not meaningful without correcting for sample size. In any large sample, some names will repeat by chance regardless of how the data was generated. The expected number of name collisions for a sample of size n drawn from a pool of N_effective distinct names follows the occupancy problem:

expected unique names = N_effective × (1 − e^(−n/N_effective)) expected collisions = n − expected unique

N_effective was estimated separately for each position from that position’s own daytime submissions using the method-of-moments estimator: N_effective = u² / (2s − u), where u is the number of unique names and s is total daytime submissions. This assumes the overnight community draws from the same underlying name distribution as the daytime community. That assumption is explicit and falsifiable. CON showed collision excesses across multiple nights, with effect sizes of 1.8%, 2.3%, and 4.1% on the three most anomalous nights. PRO worst night (February 20th): 0 observed collisions, approximately 60 expected, deficit of ~60. Statistical significance was assessed using the Poisson distribution, upper-tail for excess and lower-tail for deficit.

Sensitivity. The collision deficit finding holds unless the PRO overnight community drew from a pool of at least approximately 200,000–300,000 distinct name combinations, roughly 20–30 times the total observed PRO participant base across the full hearing. The entire PRO participant pool across five days is 9,919 unique names. A reader who disputes this should specify what pool size they would defend, and explain why that entire community was absent from every daytime window across the hearing period.

Duplicate submissions. The same name appearing multiple times are a separate question and not the subject of this analysis. Some duplication is expected in any real participation dataset; people resubmit, households share names, and common names genuinely recur. The relevant question is whether duplication rates deviate from what the population would predict. The overnight CON windows showed collision excesses consistent with resubmission or household participation. That is a different signal from the overnight PRO deficit, and it points in a different direction

What this analysis cannot determine. The geographic origin of submissions, the identity of any operator or coordinator, whether the system maintains server-side logs, and the mechanism behind any anomalous pattern. Attribution of intent from behavioral data alone is not supportable. These findings bound the probability of innocent explanation. They do not establish what the non-innocent explanation is.

I ran this analysis quickly after the GeekWire story published. There may be subtle issues in the methodology I have not caught. I am confident it is directionally correct. If you find an error, I will correct it.