TOK Essay “Without the assumption of the existence of uniformities, there can be no knowledge.”Discu
Updated: Oct 23, 2019
Word Count: 1579
Written by: Zuzanna Bednarska
In all different scientific fields, knowledge is based on observations that give rise to theories. A theory is a perception that is supported by evidence in the form of uniformities, whereas a uniformity, is described as an overall sameness.
Particular attributes can be alike in multiple aspects creating patterns, which when found, contribute to the groundwork of shared knowledge being the general information in an area. In natural sciences, trends are necessary to establish overall accepted laws that are the ground for further research.
However, since discrepancies may occur it is essential to ask to what extent can uniformities be used for establishing scientific theories. Knowledge can also be personal and thereby shape every individual differently, based on personal experiences and observations. Therefore the field of human sciences, which attempts to explain the human nature, can encounter difficulties when establishing theories. It can be problematic to decide to what extent personal knowledge create variation to the uniformities observed.
The importance of the assumption of uniformities varies between different areas of knowledge as well as specific situations and is therefore dependent on the objectivity of subject studied.
Scientists look for different trends that would support a claim and thereby result in discovery. An example of classification in the natural science was the evolution of the Periodic Table. Dmitri Mendeleev predicted properties of yet undiscovered particles by assuming the existence of a correlation between atoms. This example shows that patterns observed in nature are significant for new discoveries.
Inductive reasoning can be the key to establishing knowledge. Intuition encourages a scientist to test a hypothesis that forms out of observations and previous knowledge. One can ask if intuition is a reliable source of knowledge. When no similarity is found within research, it can be impossible to evaluate findings. For example in biology, Rosalind Franklin did not see any uniformities in the x-ray images of DNA she obtained. It was James Watson and Francis Crick who saw the patterns in the pictures that they stole from Franklin and created an adequate model of the DNA. Although their action can be questioned as unethical, they found the pattern which led to a discovery.
Nevertheless, without the detection of these uniformities, our knowledge about DNA would be limited. The most necessary part of research is the analysis of data obtained. The patterns found in the results is what generate knowledge. During my studies in the field of natural sciences, I have experienced that when uniformities are found, a conclusion can be drawn from the data collected. However, discrepancies in the overall trend can occur. In a laboratory experiment investigating the rate of osmosis, I have obtained one data which departed from the overall recorded mass of the other trials. Since the other results fitted the trend, I assumed that unusual data to be an error. Similarly in science, some discrepancies might not be considered significant when the overall data show a causation. On the other hand, what is important to consider is when the assumptions of uniformities should not be acceptable. How many discrepancies have to occur to be taken seriously? In the area of knowledge biology, it is claimed that all cells have same characteristics.
This trend has been proven through various research. Albeit several trends, abnormalities to this law have also been found. Here, reason, being one of the ways of knowing is an essential factor in determining what should be applied and how laws of natural science work. The overall findings proved a pattern between all cells with only a few discrepancies. It was, therefore, reasonable to create a law that applied to the overall case.
Without these rules, there could be no fundamental knowledge for further studies in biology. In spite of that, it cannot be forgotten that abnormalities to this law do exist and so this pattern could be abolished. Spreading awareness of abnormalities is necessary for science to evolve. Uniformities in teaching and thinking can be a dead end for the evolution of science. Therefore it is crucial to assume both existences of uniformities but also question their reliability. In the history, there have been various discoveries that opposed the accepted shared knowledge at that time.
Scientists like Nicolaus Copernicus, Antony van Leeuwenhoek or Darwin observed patterns that disproved previous theories and gave rise to new findings. These examples demonstrate that the reliability of shared knowledge can be questioned. To what extent can we trust shared knowledge if it is based on uniformities? Through assuming that some personal observations are correct, although disproving the overall belief, science can evolve and improve. Therefore assuming the presence of discrepancies can also form knowledge.
A similar situation to the past events occurred after a tsunami in Japan damaged power plant. The director Masao Yoshida ignored the orders and continued to pump seawater to the reactors against the commands. His personal knowledge, although against the previous patterns observed, was the correct action which saved the plant. In this example, the previous observations that shaped the shared knowledge were mistaken, and intuition of the director as well as diverse reasoning gave beneficial results. When applying previously observed patterns on new events it needs to be considered that some correlations will not result with the same outcome. The assumption that trends observed fit all situations might be false. The links might be drawn wrongly and mislead further studies. Through questioning uniformities truth can also be reached.
The uncertainty of uniformities can be further discussed within the field of human sciences. In this area of knowledge researchers also look for patterns to establish their hypothesis upon observed evidence. However, since humans sciences focus on similarities within human behaviour, it can be more problematic when creating trends that would apply to the entire human population. This is because every person is shaped both by the shared knowledge, but also by their own experiences. Here emotions and memories create a personal knowledge which can form differences in behaviour.
Moreover, scientists might see cause and effect relationship between data in a way that would support their claim, resulting in researcher bias. Emotions play a crucial role in the actions of a human being, and they differ from person to person. Every individual also has a unique past, and diverse memories can change the way a person reacts and thereby influence their actions. These variations are significant in the field psychology, which studies human mind and behaviour. In this area, there are no specific laws, but theories that the knowledge is based on. In psychological research, generalisability is essential.
The researchers need to consider all the aspects that might cause variation between participants and the population. For example in a study testing schema theory, Brewer and Treyns (1981) tested 30 university students to see which objects would be remembered from an office. This study, like many other psychological studies, could not be generalized to a greater population since a specific target group was used. Patterns found among participants in a specific study with specific target population cannot be assumed to fit other groups. Therefore it can be challenging to establish causation that would apply to the behaviour of all people despite their differences. Several factors might affect the personal knowledge of an individual and thereby change their behaviour creating difficulties when finding trends. Despite the differences in human behaviour some patterns can also be seen. In the study of economics correlation in the market have been noticeable. Since economics focuses on human society, the research is very sensitive when developing laws that would apply to the entire human populations. However, if the same trend has been recalled several times, theories can be created. For example, the law of demand states that the higher the price, the lower the demand will always be. This trend was seen over time, and therefore the similarity found, give rise to new knowledge, predicting the future economic change of a state. Here reason and previous knowledge played a role in determining predictions, instead of intuition. If there would be no correlation between human behaviours, each change in the society would be unpredictable, and thereby no knowledge would be able to be formed. Therefore assuming the existence of causation when analysing data is crucial when developing knowledge.
Based on the above examples it can be concluded that knowledge is established by uniformities. If patterns are not detected, it can be hard to draw reliable conclusions for the development of science. Through intuition and assumption of the existence of trends, scientists such as Mendeleev could develop theories that are now accepted as laws. However, it is necessary to acknowledge discrepancies to the overall uniformities since the shared knowledge might not always be the correct approach. There can be various interpretations and several similarities between different observations. It might not be the case that the most accepted version is the correct knowledge. The intuition of a scientist can be of major importance when establishing knowledge as shown in the example of nuclear plants in Japan. The assumption of correlations between two situations might also be misleading and result in mistakes. Therefore discrepancies should be considered when developing knowledge. Also in the area of human sciences uniformities are significant in determining theories and trends. Nonetheless one should acknowledge that human beings differ and therefore it will might be difficult to set rules that would match each individual. It is the balance between reason and intuition that drives the assumptions forward and uncover patterns that give rise to new knowledge.
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