Is Observation Both Essential and Flawed?

To say that knowledge has been produced means that a belief has been justified through evidence and reasoning by a process of shared agreement. Yet the standards for sufficient justification differ among areas of knowledge (AOKs). Knowledge can be produced through various tools, including observation, classification, and abstraction. This essay focuses specifically on observation as a tool in the production of knowledge and considers the question, “Does it matter that observation is an essential but flawed tool in the production of knowledge?” through two areas of knowledge: the natural sciences and the human sciences.

Observation always entails the possibility of error. Although it is an essential means of obtaining information about the world, it can never be entirely free from technological limitations or cognitive biases. To reach sound conclusions, therefore, the flaws in observation must either be eliminated or properly understood. The ways in which these flaws are managed differ across areas of knowledge, including the natural and human sciences.

It is also important to recognize that the inherent flaws of observation are among the driving forces behind the development of knowledge systems. Academic communities continually repeat tests, attempt falsification, and refine interpretations in order to overcome observational limitations. This ultimately leads to methodological progress and paradigm shifts, and consequently to the production of more accurate knowledge.

The natural sciences seek to construct universal explanations of natural phenomena through observation and experimentation. The necessity of observation in the natural sciences becomes clear during the process of justification: for an existing hypothesis to be accepted as knowledge, it requires empirical justification and demonstration through observation. Although these processes are important in every area of knowledge, they are especially central to the natural sciences because reproducibility must be secured while excluding subjectivity. Without a sound process of justification, a hypothesis cannot be elevated to the status of shared knowledge. Importantly, scientific verification procedures are designed not only because scientific knowledge is justified through observation, but also because observation is assumed to be imperfect.

The flaws of observation can be broadly divided into technological and cognitive flaws. Technological flaws include measurement errors, limitations of instruments, and faulty statistics. These tend to be resolved as technology advances and becomes more precise. In other words, technological flaws can be managed through more sophisticated measurement and technological development. The very nature of the natural sciences, which seek accurate explanations of nature, explains why observational technologies continue to advance. Cognitive flaws, meanwhile, include theory-laden observation, confirmation bias, and the observer effect. They can be managed in part through “organized skepticism,” including cross-validation and peer review. However, because cognitive flaws are less clearly identifiable than technological ones, they remain more difficult to control. This also helps explain why the natural sciences have developed into a system that continually examines the possibility of error rather than claiming absolute certainty.

A suitable example is the discovery of the Higgs boson at CERN. Predicted in 1964 within the framework of the Standard Model, the Higgs boson was confirmed approximately fifty years later at CERN's Large Hadron Collider (LHC). To obtain reliable results, the research teams applied a “five-sigma” standard, which the essay characterizes as an error probability approaching approximately 0.0000003%. The five-sigma threshold can be understood as an attempt to manage technological flaws through precise measurement. Ultimately, this contributed to the extraordinary scientific advance represented by the confirmed discovery of the Higgs boson. CERN also assigned the research to two separate teams in order to ensure accuracy and objectivity. The teams were unable to share information and independently designed their experiments and developed their computer programs. This was an effort to counteract cognitive flaws.

The discovery of the Higgs boson under the rigorous standards of five sigma and independent verification can therefore be seen as possible precisely because scientists began with the assumption that “observation is flawed.” This example shows that knowledge in the natural sciences is not produced simply by accumulating observations, but through a process of continually examining their potential errors. In other words, the reliability of the natural sciences does not arise from the perfection of observation, but from verification systems built on the assumption that observations may be mistaken. What matters in the natural sciences, therefore, is not observation alone, but how observations are justified and verified.

By contrast, before and after the Higgs boson discovery, several studies that applied the relatively lower thresholds of three or four sigma were later found to be incorrect. For example, in the mid-2000s, several studies reported the discovery of a new particle called the “pentaquark.” Their reported confidence levels were around four sigma, and the scientific community increasingly tended to regard the particle's existence as established. However, larger-scale follow-up experiments revealed that the findings had involved interpretive errors arising from researchers' selection bias. In contrast to the Higgs boson discovery, the supposed discovery of the pentaquark can be understood as an error resulting from researchers' comparatively insufficient attention to the epistemological limitations of data collection. The accuracy of the knowledge produced thus depends on how rigorously researchers control potential observational errors and cognitive biases.

One might object, however, that errors in observation, such as those in the preceding example, mislead the scientific community, waste research resources over long periods, and impede progress. Do flawed observations not produce unnecessary costs and delays by leading to incorrect conclusions?

Certainly, observational flaws can cause setbacks in academic research. Nevertheless, it is important to adopt a long-term perspective in which even these setbacks can eventually contribute to progress. Past errors have demonstrated the dangers of accepting observational results uncritically, leading to the adoption of stricter statistical standards and verification procedures. Thus, even if scientific progress appears to be hindered in the short term, the accumulated understanding of observational flaws can ultimately enable greater scientific advances.

In conclusion, observational flaws matter in the natural sciences not merely because errors must be prevented, but because they are a crucial force driving the continual revision and re-examination of knowledge. Ignoring observational errors increases the risk of mistaking false conclusions for truths and may even obstruct the possibility of future falsification by new theories and technologies. Recognizing such flaws therefore enables the natural sciences to function as a “self-correcting system of knowledge,” rather than settling for absolute certainty. Ultimately, the crucial issue is the process through which the reliability of observations is justified, rather than observation itself.

The human sciences investigate complex human behavior and society. The necessity of observation in the human sciences arises from the multitude of variables that cannot be controlled. Unlike the natural sciences, which emphasize variable control and reproducibility, the human sciences cannot control every variable involved. Researchers must therefore observe and interpret information gathered within diverse social and cultural contexts. In the human sciences, variables become subjects to be understood rather than factors to be eliminated.

The flaws of observation in the human sciences are generally cognitive, including theory-laden observation, the observer effect, and researcher bias. The observer effect occurs when people alter their behavior because they know they are being observed, potentially supplying researchers with distorted data. Naturalistic observation and other methods can reduce this effect, but cannot eliminate it completely. Researcher bias can also distort findings during data interpretation. Because both researchers and their subjects are human, these flaws are particularly difficult to remove completely in the human sciences, unlike in the natural sciences.

A representative example of observational flaws in human-science research is Margaret Mead's study of Samoan adolescents. At the time, Western scholarship tended to explain adolescent anxiety and rebellion as biological phenomena, whereas Mead believed cultural factors were more important. To support this view, she spent approximately nine months in Samoa observing and interviewing local teenage girls. According to her account, Samoan society was sexually open, and its culture of communal child-rearing and relatively low social pressure meant that adolescents experienced far less identity confusion or rebellion than their Western counterparts. On this basis, Mead argued that the crises of adolescence were not universal phenomena but were shaped by cultural environments. Her research subsequently had a major influence on Western society and education.

In 1983, however, the anthropologist Derek Freeman criticized Mead's research as being founded on bias and errors of interpretation. Having conducted long-term research in Samoa, Freeman presented a picture of Samoan society very different from Mead's. He argued that Samoan society was marked by violence and strict sexual norms, and that Mead had mistaken local girls' sexual jokes for factual accounts while overlooking the complexity of their culture. These flaws can be interpreted as examples of the observer effect, theory-laden observation, and confirmation bias. In other words, Freeman's criticism raised the question of whether Mead's observations had adequately justified her knowledge claims.

Following this debate, methodological changes also emerged in the human sciences. One prominent example is the development of triangulation. Triangulation is a method of examining a phenomenon from multiple angles by drawing on different sources, researchers, theories, or methodologies. Mead's study largely relied on a single researcher to collect and interpret the data, making it difficult to escape individual bias. Triangulation, by contrast, examines the same phenomenon from diverse perspectives, preventing one researcher's interpretation from monopolizing knowledge. Mead's case thus shows how flaws in observation can stimulate methodological advances in the human sciences.

An objection remains, however: in the human sciences, observational flaws are often difficult to reassess through replication. Unlike the natural sciences, human societies change over time, limiting reproducibility. Indeed, Samoan society underwent considerable changes between Mead's and Freeman's periods of research, influenced by factors including the spread of Christianity and colonial rule. It is therefore unclear whether Freeman's criticism identified genuine errors in Mead's observations or simply reflected his observation of Samoa in a different historical period. This illustrates how the justification of knowledge in the human sciences depends more strongly on context than it does in the natural sciences.

Nevertheless, what matters is that the debate between Mead and Freeman itself advanced the human sciences. Furthermore, contemporary researchers have criticized not only Mead but also Freeman for selectively using evidence or failing to account adequately for differences in the periods studied. This demonstrates that the reliability of observation in the human sciences is not guaranteed by the authority of any particular researcher; rather, it develops through continuous criticism and reinterpretation.

Taken together, these examples suggest that the human sciences have come to emphasize the fact that every observation occurs within a particular context, rather than seeking to establish one definitive truth. Both Mead's research and Freeman's criticism were influenced by their historical and cultural backgrounds. This suggests that observation cannot be entirely neutral, since those who produce knowledge in the human sciences also exist within various social and cultural contexts. Accordingly, observation in the human sciences is something to be interpreted rather than a flaw to be completely eliminated. Whereas the natural sciences seek to minimize errors by controlling variables, the human sciences seek to understand their meaning by considering diverse contexts. The importance of observational flaws in the human sciences therefore lies not merely in preventing errors, but in recognizing that observation always takes place from a particular perspective and within a particular context.

Observation is one of the essential processes in the production of knowledge. It is necessary to provide empirical evidence and demonstration for existing theories. No matter how persuasive or reasonable a hypothesis may seem, observation is required because it is impossible to predict every variable operating in the real world.

Nevertheless, as the discovery of the Higgs boson and the studies in Samoa show, observation is an inherently flawed tool. In the natural sciences, these flaws appear as instrumental errors, statistical limitations, and researchers' cognitive biases. In the human sciences, by contrast, they often arise from researchers' interpretations, the observer effect, and complex sociocultural contexts. Observational flaws therefore matter in both areas, but their nature and the methods used to address them differ. The natural sciences attempt to eliminate errors by controlling variables as much as possible and ensuring reproducibility; the human sciences seek to interpret and understand errors through multiple perspectives and methodologies. In other words, the same observational flaw may be treated as something to eliminate in the natural sciences but something to interpret in the human sciences.

If observation were a perfect tool, therefore, academic disciplines might actually find it harder to advance. Once observation became a flawless means of producing knowledge, the natural sciences would no longer need to continually test and challenge existing theories, while interpretation and criticism from different perspectives would lose their significance in the human sciences. In reality, however, every observation has limitations, so researchers continually review and revise their conclusions. The emergence of stricter verification standards in Higgs boson research and the development of methods such as triangulation following the Mead–Freeman debate can both be understood in this context.

Of course, there are cases in which the harm caused by observational flaws exceeds the benefits. The actual costs and benefits can vary considerably from case to case. If we interpret observational flaws only as drivers of progress, we risk overlooking their immediate social costs and ethical consequences. Recognizing the importance of such flaws therefore does not mean that we should leave them unaddressed. On the contrary, methodological rigor must be maintained at every stage in order to minimize their real-world costs.

In conclusion, it matters greatly that observation is both essential and flawed in the production of knowledge. Its flaws matter not only because they can lead to incorrect conclusions, but also because they enable systems of knowledge to correct and develop themselves. Ultimately, recognizing the limitations of observation is not merely an attempt to reduce error; it is a necessary condition for producing more reliable knowledge.