Objectivity
Objectivity: The Cornerstone of Analytic Tradecraft in Intelligence and Investigations
Objectivity is a core skill for intelligence professionals, Open-Source Intelligence (OSINT) analysts, and cybercrime investigators. At its heart, objectivity means approaching analysis with an impartial, evidence-first mindset—working to eliminate personal bias and outside influence. Analysts who practice objectivity offer decision-makers information that’s both accurate and unbiased, which is essential whether the setting is national security or corporate threat intelligence.
“Analytic objectivity and sound intelligence tradecraft ensure our nation’s leaders receive unbiased and accurate intelligence to inform their decisions."
This article takes a closer look at what objectivity really means, why it goes beyond simple “neutrality,” how it's embedded in formal standards like U.S. Intelligence Community Directive 203, and why it’s so important across both government and private investigations.
Defining Objectivity in Analysis
In intelligence and investigative work, objectivity means sticking to a fact-based perspective throughout the process. An objective analyst follows the evidence wherever it leads—without letting personal opinions, preferences, or external pressures influence their judgment.
According to standards in the U.S. Intelligence Community, analysis must be conducted “with objectivity and with awareness of one's own assumptions and reasoning,” using specific methods to identify and reduce bias. In practice, this means constantly checking that your conclusions are grounded in verifiable data, not shaped by preconceived ideas. Objectivity is key for making intelligence products credible and trustworthy. The Office of the Director of National Intelligence (ODNI) highlights that objectivity and strong analytic tradecraft ensure decision-makers receive intelligence they can rely on.
The same applies in digital investigations. Investigators are expected to remain objective throughout the investigation, avoiding biases that might influence their findings or interpretations. Whether you’re drafting a CIA intelligence estimate or a private cybersecurity report, objectivity underpins the integrity of the entire process.
Objectivity vs. Neutrality: An Active Pursuit of Truth
It’s important to understand that objectivity is not the same as neutrality. Neutrality means not taking a side or withholding judgment. Objectivity, on the other hand, requires making judgments—but only when they’re backed by solid evidence and logic.
An analyst can remain objective and still come to a strong conclusion if that’s what the facts support. In fact, true analytic objectivity involves actively challenging assumptions, considering alternative explanations, and “speaking truth” even when it’s uncomfortable. As tradecraft expert Richards J. Heuer, Jr. explains: “Objectivity is gained by making assumptions explicit so that they may be examined and challenged, not by vain efforts to eliminate them from analysis.” (https://www.ialeia.org/docs/Psychology_of_Intelligence_Analysis.pdf) In other words, objectivity comes from surfacing your assumptions and testing them, not pretending they don’t exist.
True objectivity is an active, ongoing process. Analysts need to keep questioning their own conclusions, look for information that might contradict their current thinking, and weigh different hypotheses about what’s going on. It’s similar to the scientific method: propose a theory, then try to disprove it.
Unlike neutrality—which might give equal weight to all viewpoints, even when the evidence clearly favors one side—objectivity means giving more weight to stronger evidence. For example, an objective OSINT investigator won’t just “stay neutral” between two competing narratives. Instead, they’ll search for verifiable facts, and if one hypothesis is clearly better supported, they’ll state that—while still noting any uncertainties. As media ethicists put it: “Neutrality is nice, but not at the expense of objectivity."
(https://www.commondreams.org/views/2012/10/25/objectivity-does-not-mean-neutrality-danger-false-equivalency-media). In short, loyalty should be to truth and evidence, not to treating all sides as equal when they’re not.
Because of this, objectivity actually takes more effort than neutrality. A neutral approach might just mean withholding judgment. But objectivity demands that analysts keep updating their conclusions as new information comes in. It calls for tough self-questioning and asking things like:
“Why do I believe this?”
“Could I be wrong?”
“What else could explain this?”
And most importantly, it requires being open to changing your mind if the facts call for it. In the end, objectivity isn’t passive. It’s not about staying on the fence. It’s a dynamic, evidence-driven way of seeking the truth—one that brings both rigor and integrity to every stage of the analysis.
Objectivity in Analytic Standards (ICD 203 and Beyond)
The importance of objectivity is formally built into professional frameworks and standards. One key example is the U.S. Intelligence Community Directive 203 (ICD-203), which sets analytic standards for all U.S. intelligence agencies. Objectivity is the very first of the five core standards listed. It requires analysts to remain impartial, be aware of their own biases, and actively consider different viewpoints and conflicting evidence in their work.
Analysts are expected to use structured reasoning techniques and practical methods that help uncover and address bias. They must avoid being unduly influenced by earlier judgments or the pull of conventional wisdom. In practical terms, this means that intelligence reports need to clearly separate facts from the analyst’s assumptions or interpretations. They should also show that the analyst explored multiple possibilities instead of locking in too early on a single explanation.
ICD-203 further reinforces objectivity through its Analytic Tradecraft Standards. For instance, one standard requires analysts to "incorporate analysis of alternatives." This ensures they go beyond their first instincts and actively explore different explanations. Another standard is to clearly express uncertainties and confidence levels, instead of suggesting more certainty than the facts can support. These practices help expose hidden assumptions and ensure transparency, so others can see how conclusions were reached and verify that the analysis wasn’t biased or one-sided.
Beyond ICD-203, similar standards exist across other organizations. Intelligence agencies around the world stress impartial analysis in their training and doctrines. In the United States, the Intelligence Reform and Terrorism Prevention Act (IRTPA) of 2004 called for intelligence to be objective, independent of political considerations, and based on all sources.
In the United Kingdom, guidance documents for OSINT tradecraft emphasize the importance of unbiased investigative methods. They also outline structured techniques for minimizing bias and preserving objectivity (Combatting Bias in OSINT: Strengthening the Integrity of UK Internet Investigations).
Across the board, professional codes of ethics, for example, those guiding law enforcement analysts or financial crime investigators, include firm commitments to honesty, objectivity, and accuracy. Whether spelled out in formal policy or reinforced through professional culture, the message is clear: objectivity is not optional. It’s essential for credible, trustworthy analysis.
The Role of Objectivity in Government Intelligence Work
In the world of government intelligence, objectivity is more than just a guiding principle. It’s a practical necessity. Agencies like the CIA, NSA, and FBI, along with their counterparts, exist to support policy-makers by providing information, not by promoting agendas. As the Office of the Director of National Intelligence (ODNI) explains, the Intelligence Community’s job is to deliver “timely, insightful, objective, and relevant intelligence to inform decisions on national security,” not to shape or advocate policy.
Staying objective in government settings often means resisting political or organizational pressure. Analysts are directed to remain independent from political influence. If intelligence evidence contradicts a publicly stated position from a policy-maker, analysts still have a duty to present that evidence truthfully. They cannot adjust their findings just to make them more acceptable. This can be difficult in practice, especially under intense scrutiny and high stakes.
To protect analysts from such pressure, ICD-203 requires each agency to have an Analytic Ombudsman. This official serves as a resource for analysts who feel their objectivity is being threatened. If an analyst believes their work is being compromised by politicization or other biases, they can go to the Ombudsman for support or a formal review. The Ombudsman can investigate and help resolve those concerns, underscoring how seriously objectivity is treated in government intelligence.
There are well-known cases where a lack of objectivity led to major intelligence failures. One of the most significant was the pre-war intelligence on Iraq’s weapons of mass destruction (WMD). After the fact, investigations revealed that analysts had become too attached to the assumption that Iraq was hiding WMD. As a result, they gave too much weight to weak evidence that supported this belief and ignored signs pointing the other way (https://georgewbush-whitehouse.archives.gov/wmd/text/report.html).
The problem wasn’t just faulty data. It was that analysts treated a working theory as if it were fact. Over time, they began to accept supporting evidence without enough scrutiny and dismiss anything that challenged the assumption. Investigators concluded that this happened partly because analysts were working in an environment where skepticism wasn’t encouraged.
This failure became a turning point. The Intelligence Community began placing even greater importance on structured techniques like alternative analysis, “red teaming,” and internal debates. These methods are now essential for encouraging diversity of thought and making sure assumptions don’t go unchallenged. By fostering debate and embracing different perspectives, agencies reduce the risk of blind spots and strengthen objectivity.
There are also success stories where objectivity made all the difference. For example, in the early 2000s, U.S. intelligence analysts correctly assessed Libya’s secret nuclear weapons program. This breakthrough happened because analysts approached new evidence with an open mind, even when it conflicted with past assumptions. By staying focused on the facts, they recognized that Libya was ready to make a deal and disarm. Their accurate assessment helped guide a successful nonproliferation outcome.
This example shows how objectivity leads to better decisions. It gives agencies the flexibility to adjust when new information emerges. Rather than clinging to outdated narratives, they can respond to reality as it is. In government intelligence, objectivity is what keeps the system honest. It ensures that national security decisions are based on facts, not wishful thinking or political pressure, and that’s crucial for both effective policy and real-world success.
Objectivity in Private Sector and OSINT Investigations
Outside of government, objectivity is just as important in the private sector and OSINT community, even though the settings are different. Today, many businesses conduct their own intelligence and investigative work. This includes cybersecurity threat intelligence, due diligence research, and digital forensics in cybercrime cases. In these areas, objectivity is key to good decision-making.
Organizations rely on their analysts to deliver clear, accurate findings. A single biased or incorrect report can lead to costly consequences. These might include underestimating a cyber threat, missing signs of fraud, or harming an innocent party’s reputation. That’s why companies, like intelligence agencies, have a strong incentive to build a culture of impartiality and evidence-based reasoning.
Still, maintaining objectivity inside private organizations can be tough. Business politics or competing interests may influence an analyst’s work (whether intentionally or not). For instance, if a security analyst traces a breach to a country where the company’s CEO is negotiating a major deal, there may be subtle pressure to downplay the findings. In that moment, an objective analyst must speak plainly, providing leaders with a clear and accurate picture of the risk, even if it's uncomfortable.
Biases can also creep in through corporate culture. A team might lean too heavily on a preferred source or ignore contradictory data out of habit. Analysts may also fall back on personal comfort zones, sticking to familiar tools or assumptions. These patterns can lead to skewed assessments, like underestimating a real threat or overreacting to a minor one. In either case, the business loses.
To prevent this, many leading corporate intelligence teams work hard to promote objectivity. They provide training to help analysts recognize and overcome bias, and they build workflows that emphasize information verification and source validation. Some adopt internal policies modeled on ICD-203, where analysts are expected to cite sources, flag assumptions, and consider alternative explanations in their reporting. Others incorporate structured analytic techniques originally developed for government intelligence to critically examine internal findings. The result is a culture where intelligence is rigorously tested before it reaches decision-makers—and where leadership can trust that decisions are based on a well-rounded, accurate view of the facts.
The open-source intelligence (OSINT) community also embraces objectivity as a foundational value. Since OSINT relies on publicly available data—which may include false or misleading information—analysts must be especially diligent about verifying facts. Skilled OSINT practitioners know that good investigations demand cross-checking sources and adjusting conclusions as new evidence comes in. The standard is clear: an investigation should account for all available information, not just what supports an early theory.
For example, when reviewing social media posts about a conflict, an OSINT analyst must guard against confirmation bias—the tendency to focus on data that aligns with their expectations. Practices such as searching for contradictory information, checking source reliability, and comparing data across independent platforms help reduce this risk. Some of the most respected OSINT case studies—like those from Bellingcat or independent journalists—stand out because of their commitment to evidence-based reporting and their refusal to follow popular narratives without proof. In all investigative work, whether in a national agency, a private cyber intelligence team, or a grassroots OSINT project, objectivity is what turns speculation into reliable analysis.
Challenges to Maintaining Objectivity
Objectivity might sound simple in theory, but in practice, it’s not always easy to maintain. One major challenge comes from the way human brains process information. We all rely on mental shortcuts that can lead to bias. Analysts often work with incomplete, ambiguous, or overwhelming amounts of data, and the brain naturally tries to make sense of this information by fitting it into familiar patterns. Without safeguards, it’s easy to fall into common cognitive traps. These include:
Confirmation bias: giving extra weight to information that supports existing beliefs.
Anchoring: over-relying on the first piece of information received.
Availability bias: giving more importance to data that is easily accessible.
In OSINT investigations, for instance, the sheer amount of data online can actually make bias worse. Analysts might latch onto the first plausible narrative they find and ignore other data that doesn’t fit. As one OSINT guide warns, if investigators only search for information that proves their hypothesis, they risk missing critical evidence. Just being aware of these tendencies can help. A good analyst will intentionally search for opposing viewpoints to test their conclusions.
Pressure is another big factor. In national security crises or high-stakes cyber events, the pace is fast and expectations are high. Under tight deadlines, analysts might settle for a quick answer instead of questioning each piece of the puzzle. If clients or leadership are looking for decisive guidance, analysts might feel tempted to give them what they want instead of presenting a more uncertain or complex reality.
Groupthink can also be a problem. In urgent or politically sensitive environments, dissenting voices may be suppressed. Analysts may hesitate to challenge the team’s direction. This was a major issue in the 2002 Iraq WMD intelligence failure. Analysts, under pressure and working from long-held assumptions, didn’t feel empowered to question the prevailing narrative. They accepted weak evidence that supported their theory and dismissed conflicting data.
External influences can also create bias. Politicization is one example. If analysts know that a certain conclusion might upset leadership or contradict official policy, they may unconsciously shift their analysis. In the private sector, fear of jeopardizing deals or damaging relationships can lead analysts to soften their findings. Even personal experiences and emotions can affect judgment. A cybersecurity analyst with strong negative feelings toward a particular hacker group may over-attribute incidents to them, even without clear proof.
The key to staying objective is constant self-reflection. Analysts should regularly ask themselves, “Am I being influenced by something other than the data?” They also benefit from peer feedback. A strong analytic team encourages mutual critique—not to tear each other down, but to strengthen the overall quality of the work. Ultimately, the barriers to objectivity include mental habits, emotional pressures, organizational culture, and time constraints. Overcoming these requires ongoing effort and discipline. Analysts describe objectivity as a mindset, one that must be practiced daily.
The good news is that awareness and structure can help. Organizations that prioritize objectivity put safeguards in place, such as red-team reviews or independent ombudsmen. Individual analysts can develop habits like:
Listing their assumptions explicitly
Seeking out devil’s advocate perspectives
Being open to saying “I was wrong” when new evidence emerges
These habits act as a defense against bias. In high-stakes environments where the cost of error is high, they help ensure that analysis remains rooted in reality.
Tools and AI: Aiding but Not Replacing Human Objectivity
Modern analysts have more support than ever in maintaining objectivity, thanks to a growing array of tools and technologies. From software that tracks sources and organizes evidence to AI algorithms that process massive datasets, technology can help reduce bias and human error in certain areas.
For example, structured analytic tools such as Analysis of Competing Hypotheses (ACH) software prompt analysts to systematically weigh multiple possibilities. These tools visually map which pieces of evidence support or contradict each hypothesis, helping analysts avoid tunnel vision and favoring one narrative too quickly. Collaborative platforms also play a role by enabling real-time peer review and teamwork. When multiple investigators contribute to a single analysis, it brings in diverse viewpoints that sharpen objectivity.
Even simple tools like bias checklists and decision matrices can make a difference. They remind analysts to double-check source credibility, label their confidence levels, and ensure their conclusions are evidence-based.
The Promise and Pitfalls of AI in Analysis
Artificial Intelligence (AI) has introduced both exciting possibilities and new risks for objectivity. On one hand, AI systems can scan and analyze information at speeds no human could match. These tools can detect patterns, surface anomalies, and process large volumes of text or imagery, which might help analysts identify issues they would otherwise miss.
For instance, natural language processing can review thousands of social media posts to detect emerging narratives or shifts in sentiment. Because AI doesn’t experience fatigue or emotional bias, some believe it can offer more impartial initial findings. Generative AI tools, including large language models, can also assist with tasks like summarizing long reports, translating content, or suggesting alternative interpretations of data (https://warontherocks.com/2024/10/ai-and-intelligence-analysis-panacea-or-peril).
However, AI is no substitute for human objectivity. In reality, AI tools come with their own biases and limitations. These systems reflect the assumptions and flaws present in their training data and algorithms. If that data is skewed, the AI will be too—even in subtle or unexpected ways. Machine learning systems can amplify historic biases, misjudge certain threat patterns, or miss key signals because of blind spots in the training set (https://www.intelligence.gov/artificial-intelligence-ethics-framework-for-the-intelligence-community).
Generative models, in particular, are known to produce “hallucinations”—outputs that sound plausible but are incorrect. Since these models do not actually understand the content they generate, they might miss crucial context or nuance. This creates a risk that human users could over-trust AI summaries that omit key insights or misrepresent findings.
The Irreplaceable Role of Human Judgment
AI may assist, but it does not replace the need for human interpretation and critical thinking. As analysts and intelligence leaders have emphasized, tools should be held to the same standards of rigor as human-generated assessments. Every AI-generated insight must be vetted: sources should be verified, assumptions scrutinized, and biases considered. For example, if an AI model flags a cyber threat actor, an analyst still needs to check that assessment against known facts, evaluate its source data, and assess whether any gaps exist in the training model. The AI provides input, but the analyst remains the decision-maker.
Some intelligence agencies are now developing ethical guidelines and oversight systems for how AI is used. The U.S. Intelligence Community, for instance, has created an AI Ethics Framework that emphasizes the importance of mitigating unintended bias and aligning AI tools with ICD-203’s standards for human-led objectivity.
In the future, we may see more sophisticated “analytic assistant” tools. These could suggest alternative hypotheses or run automated bias checks on drafts. But even the most advanced systems will require human oversight. Objective analysis still depends on people applying judgment, understanding context, and being accountable for their conclusions.
Techniques and Mindsets to Strengthen Objectivity
What can analysts do, practically speaking, to stay objective? The analytic tradecraft field offers a variety of proven techniques and mindsets that help reduce bias and promote impartial reasoning.
Structured Analytic Techniques (SATs)
These are formal, disciplined methods that help analysts think critically and avoid cognitive traps. One widely used example is Analysis of Competing Hypotheses (ACH), which forces analysts to consider multiple possible explanations and test each against the available evidence (be on the lookout for upcoming articles on these techniques!). Others include:
Red Team / Devil’s Advocacy: Assigning a team member to argue an opposing view to challenge the prevailing interpretation.
Key Assumptions Check: Listing every underlying assumption and testing it for accuracy.
These techniques, pioneered in intelligence analysis and now used in business as well, have shown measurable success in improving objectivity.
Collaborative Analysis and Peer Review
Bringing in multiple analysts—especially from diverse backgrounds or specialties—helps reveal blind spots. Collaboration might involve co-authoring reports, inter-agency exchanges, or simply asking a colleague to review findings. The key is to normalize respectful questioning and debate.
Comments like “Have you considered this alternative source?” or “What about this contrary angle?” can sharpen the analysis. A team culture that encourages constructive skepticism helps prevent bias from going unchecked.
Transparency and Documentation
One of the simplest ways to stay objective is to clearly document sources, methods, and reasoning. Writing down how conclusions were reached forces analysts to articulate their logic and creates an audit trail others can review.
For example, stating “Based on Assumption A, we interpret Evidence B as supporting Conclusion C” makes it easier to revisit or question that chain of reasoning later. Many intelligence products now include appendices or footnotes that explain the analytic process in detail.
Bias Awareness Training and Checklists
Understanding common cognitive biases helps analysts catch them in action. Some common tools include checklists with prompts like:
“Did I explore alternative explanations?”
“What evidence would disprove my conclusion?”
“Am I ignoring something because it feels inconvenient?”
Heuer’s Psychology of Intelligence Analysis remains a foundational resource here. It promotes techniques like externalizing thought (using charts or matrices) and delaying firm conclusions until multiple views have been considered.
Cultivating an Objective Mindset
Beyond tools and checklists, objectivity also comes down to attitude. Analysts who maintain objectivity tend to show:
Curiosity: a drive to explore different angles and unexpected possibilities.
Humility: a recognition that even seasoned professionals can be wrong.
Skepticism: a habit of questioning sources, assumptions, and conclusions.
These traits can be fostered by leadership modeling and organizational culture. When senior analysts admit uncertainty or welcome feedback, it encourages others to do the same. Creating space for questions, rethinking, and even saying “I don’t know” helps reinforce the goal of finding the truth—not just proving a point.
Putting It All Together
When combined, these techniques and mindsets offer a powerful defense against bias. For instance, an analyst might start by brainstorming alternate explanations, run a key assumptions check, then gather data. Halfway through, they might ask a colleague to red-team the initial findings. Finally, they would document all sources and run through a bias checklist before submitting the product.
This may sound time-consuming, but it can be scaled to fit the task. Even a quick alternate hypothesis sketch and a peer review can greatly improve objectivity. With practice, these techniques become second nature—built into the analyst’s workflow. The result is analysis that stands up to scrutiny, earns trust, and helps decision-makers act on solid ground.
Real-World Lessons and Case Studies
To truly understand the value of objectivity, it's helpful to look at cases where it shaped outcomes. One well-known example is the Iraq WMD intelligence failure, where the lack of objectivity led to major errors. That case led to reforms such as the creation of ICD-203, designed to strengthen analytic rigor and prevent similar mistakes.
But consider a different, more relatable scenario: a corporate investigation into repeated network breaches. Investigators might quickly settle on the theory that a disgruntled employee is responsible—especially if the company recently experienced layoffs. If they follow that narrative too early, they might overlook signs pointing to an outside hacking group. Now imagine that one team member takes a step back and insists on reviewing all the malware signatures objectively. They recognize patterns associated with a known cybercrime gang. The investigation shifts direction, focuses on external access, and ultimately uncovers the real perpetrators. This prevents a major data theft that would have occurred had the investigation stuck with its internal bias. While this is a composite example, it reflects countless real cases where objectivity pointed investigators toward the right answer while bias could have led them astray.
In law enforcement and forensics, objectivity is even more critical—it can mean the difference between justice and injustice. There have been instances where forensic examiners, under pressure to deliver convictions, interpreted evidence in biased ways that led to wrongful arrests. To prevent this, many forensic labs now use double-blind procedures. In these cases, examiners don’t know which side requested the analysis, which helps preserve impartiality. Peer review has also become more common.
A study by the FBI showed that even subtle expectations can influence how experts interpret fingerprints or DNA, a phenomenon called contextual bias. The solution is to structure analysis workflows so that examiners evaluate the raw data first, before learning the broader context. This principle also applies to intelligence and OSINT. For example, an OSINT investigator might delay reading news headlines about their topic until after they’ve reviewed the raw data. This prevents sensational narratives from influencing the analysis too early in the process.
There are also powerful examples where objectivity led to groundbreaking investigations. Crowdsourced OSINT projects, like those from Bellingcat and academic research labs, demonstrate how transparency and objectivity can uncover the truth. These teams often rely solely on public data, and they make their entire analytic process available for review. One notable example is the investigation into the 2014 downing of Malaysian Airlines Flight MH17 over Ukraine. OSINT researchers pieced together satellite imagery, geolocated photos, and social media posts to reconstruct the incident. Their findings were later confirmed by official government investigations. Their credibility came not from institutional authority but from an objective and transparent methodology. Every step was based on verifiable evidence that anyone could follow.
These real-world examples show a consistent theme: objectivity is often the difference between success and failure. Analysts who stay grounded in facts (even when the truth is uncomfortable) are better equipped to find accurate answers. When objectivity fades, the risks rise: errors go unnoticed, threats are misinterpreted, and actions may be misdirected. In intelligence, law enforcement, or corporate security, the cost of losing objectivity can be immense.
Conclusion
For professionals working in intelligence, OSINT, and cyber investigations, objectivity is more than just a concept. It is a daily practice and a professional ethic. It means placing evidence above ego, constantly examining your own assumptions, and using every available method to reduce bias.
Objectivity is not the same as neutrality. Neutrality avoids judgment. Objectivity makes judgments, but only when they are rooted in evidence and thoughtful reasoning. An objective analyst does not shy away from conclusions. They arrive at them carefully, based on a process that is transparent, honest, and self-critical.
In today's analytic landscape, credibility depends on objectivity. Government frameworks like ICD-203 establish standards that protect it. New technologies aim to support it. And real-world successes, from OSINT investigations to corporate threat responses, continue to prove its value. But in the end, objectivity is sustained by people. It depends on the mindset of the analyst and the culture of the team.
Analysts who adopt an objective approach, supported by leaders and peers who value integrity over comfort, are the ones who produce the most trusted work. They can navigate bias, push back against pressure, and provide insights that lead to real understanding and meaningful action.
Objectivity is also a skill that improves with practice. It grows stronger through structured techniques, open collaboration, and a willingness to learn from missteps. It is both a tool and a responsibility. Analysts owe it to their clients, their organizations, and the truth itself.
When objectivity guides the analytic process, the outcome is intelligence that can stand up to scrutiny. It offers clear, reliable direction in uncertain environments. And most importantly, it helps decision-makers act with confidence, knowing that what they are relying on was built with care, discipline, and respect for the facts.
