Malcolm Rivera
The human sciences, including psychology, social and cultural anthropology, economics, and geography, study the social, cultural, and biological aspects of human existence. A key question in these fields is what constitutes good evidence. While the natural sciences rely on experimentation and the testing of hypotheses to establish scientific laws, the human sciences often uncover trends rather than laws due to the unique challenges of studying human behaviour. For example, human experiments cannot be repeated under identical conditions, and the very act of predicting a behaviour can affect the prediction. As a result, human scientists may rely on other forms of knowledge, such as anecdotal evidence, which must be carefully evaluated to avoid fallacies and biases.
| Characteristics | Values |
|---|---|
| Repeatable | Same results should be obtained if tests are rerun |
| Variety | Wide variety of evidence is better than anecdotal evidence |
| Reproducible | Reproducible evidence is better than evidence that can't be reproduced |
| Transparency | Relevant policy concerns should be identified ex ante for transparency |
| Complementary | Natural scientists can offer complementary knowledge to explain human behaviour |
| Iterative | Competition of "plausible rival hypotheses" |
| Simplicity | The simplest comprehensive description of the evidence is most likely correct |
| Null Hypothesis Testing | Null hypothesis statistical testing helps handle controversial issues in statistics |
| Bayesian Statistics | Combination of prior knowledge and current experimental data |
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What You'll Learn
Repeatable phenomena
In the human sciences, which encompass fields such as psychology, social and cultural anthropology, economics, and geography, the concept of repeatable phenomena takes on a unique character. Human behaviour and social dynamics are inherently complex and influenced by a multitude of variables, making it challenging to control all factors and replicate experiments with identical conditions. This complexity underscores the importance of adopting a nuanced approach to defining "repeatable phenomena" in the context of human sciences.
One approach to addressing this challenge is to focus on patterns and trends rather than seeking absolute laws. Human sciences often uncover trends and correlations rather than establishing rigid laws with precise predictive capabilities. For example, in economics, a correlation may be observed between inflation and unemployment rates, but it does not necessarily imply a causal relationship. Human scientists may employ methods such as statistical analysis and modelling to identify recurring patterns in data, even if exact replication of experiments is not feasible.
Additionally, the human sciences may draw upon a variety of evidence, including anecdotal evidence, systematic studies, and firsthand accounts. While anecdotal evidence should not be the sole basis for conclusions, it can serve as a valuable indicator or complement to other forms of evidence. For instance, in psychology, patterns observed in the behaviour of multiple individuals can contribute to the formulation of theories and interventions.
In conclusion, the notion of repeatable phenomena in the human sciences involves a recognition of the inherent complexity of human behaviour and social systems. While exact replication of experiments may not be achievable, the focus shifts towards identifying recurring patterns, trends, and correlations. By employing a range of evidence, including systematic studies and anecdotal reports, human scientists can enhance the robustness of their findings and contribute to a deeper understanding of human behaviour and its underlying dynamics.
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Variety of evidence
The human sciences, including psychology, social and cultural anthropology, economics, and geography, study the social, cultural, and biological aspects of human existence. While the human sciences use the scientific method to test the validity and reliability of hypotheses, they differ from natural sciences in that they uncover trends rather than laws. This is because human experiments cannot be repeated under the same conditions, and the very act of predicting may affect the prediction. As a result, human scientists often focus on explaining things as they happen rather than making predictions.
When it comes to evidence, a good scientist adopts a skeptical attitude, demanding "good evidence" before accepting a claim. Good evidence is often characterised by its reproducibility. That is, if other scientists rerun the first scientist's tests, they should obtain the same results. Reproducible evidence is more reliable than anecdotal evidence, which is based on personal stories or individual experiences.
In the 1950s, Rudolf Carnap proposed three categories for evaluating evidence:
- Classificatory: Whether the evidence confirms the hypothesis.
- Comparative: Whether the evidence supports one hypothesis more than another.
- Quantitative: The degree to which the evidence supports a hypothesis.
Additionally, Achinstein identified four concepts of evidence:
- Epistemic-situation evidence: Evidence relative to a given epistemic situation.
- Subjective evidence: Evidence from the perspective of a particular person at a particular time.
- Veridical evidence: Evidence providing a good reason to believe a hypothesis is true.
- Potential evidence: Evidence indicating a high probability of a hypothesis being true.
In the human sciences, it is important to distinguish correlation from causation to avoid erroneous conclusions. For example, the Phillips curve in economics showed a correlation between inflation and unemployment rates, but increasing inflation did not lead to reduced unemployment in practice.
Furthermore, the human sciences often deal with complex and context-specific social realities, making it challenging to predict future outcomes with certainty. While criteria like Bradford Hill's criteria for judging causal effects can be applied, they may not capture the mechanisms of causality or generalisability.
Overall, good evidence in the human sciences involves a variety of factors, including reproducibility, a critical evaluation of different types of evidence, distinguishing correlation from causation, and acknowledging the limitations of predictions and generalisations.
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Correlation vs causation
The human sciences study the social, cultural, and biological aspects of human existence. They are classified as a science because they use the scientific method to test the validity and reliability of hypotheses. However, the human sciences differ from natural sciences in the interpretation of the word "science".
When it comes to good evidence in the human sciences, it's important to consider the distinction between correlation and causation. Correlation refers to a statistical association or relationship between variables, where they change together or covary. In other words, when one variable changes, so does the other. This relationship can be positive or negative. A positive correlation indicates that as one variable increases, the other also increases, and vice versa. On the other hand, a negative correlation means that when one variable increases, the other decreases, and vice versa. The strength and direction of the relationship between variables can be quantified using a correlation coefficient, which ranges from +1.0 to -1.0.
Causation, on the other hand, implies a cause-and-effect relationship between variables. It suggests that a change in one variable directly leads to a change in another variable. In other words, one event or occurrence is the result of another event. While it may seem straightforward in theory, establishing causation in practice can be challenging. This is because correlation does not always imply causation. Just because two variables are correlated does not necessarily mean that one caused the other. There may be other factors or variables at play that influence the relationship.
For example, let's consider the correlation between recreational drug use and psychiatric disorders. It could be that drug use leads to psychiatric disorders, or it could be that individuals with pre-existing psychiatric disorders turn to drugs as a form of self-medication. This is known as the "correlation does not imply causation" principle, which highlights that simply observing an association between two variables is not sufficient to establish a cause-and-effect relationship. To demonstrate causation, it is crucial to establish a directional relationship with no alternative explanations.
To establish causation, various methods and criteria are employed. In the health sciences, Bradford Hill's criteria are often used to judge causal effects. These criteria include considering temporal relationships, dose-response relationships, and other factors. Additionally, statistical methods such as the Granger causality test and convergent cross-mapping can be used to test for causality. However, it is important to recognize that even with these tools, determining causation can be complex, especially in fields like the social sciences, where controlled experiments may not always be feasible.
In summary, while correlation and causation are related concepts, they are distinct. Correlation describes the statistical association between variables, while causation implies a direct link where one variable causes a change in another. By understanding this distinction, researchers can more effectively evaluate sources, interpret scientific findings, and develop targeted policies and programs.
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Complementary knowledge
The human sciences study the social, cultural, and biological aspects of human existence. While the natural sciences can provide complementary knowledge to explain human behaviour, the human sciences can explain many phenomena that other areas of knowledge cannot. For example, in economics, human scientists may predict trends rather than laws. Economist Phillips suggested in the 1960s that there was a "stable relationship between the rate of inflation and the rate of unemployment." However, when governments attempted to reduce unemployment by allowing inflation to rise, they ended up with both rising inflation and rising unemployment. This example demonstrates that correlation does not always imply causation, and theories in the human sciences are often better at explaining events as they happen rather than making predictions.
The human sciences face challenges in conducting experiments and making predictions due to the unique nature of human subjects. Participants in human experiments can never be in exactly the same condition, and their knowledge and expectations can change over time. Additionally, the very act of predicting can influence the prediction itself. These factors make it difficult for human scientists to claim with certainty that something is a scientific fact.
The interpretation of evidence also differs between the human and natural sciences. In the human sciences, anecdotal evidence, such as firsthand stories or single observations, can be given more weight than systematic search or testing results. Scientists should maintain a sceptical attitude and demand good evidence before adopting beliefs. Reproducible evidence is considered stronger than anecdotal evidence, as repeating experiments allows for the validation or refutation of results.
Overall, while the human sciences may not always provide the same level of certainty or predictability as the natural sciences, they offer valuable insights into human behaviour, social trends, and cultural phenomena. The complementary knowledge from the natural sciences can enhance our understanding, but it is essential to recognise the unique challenges and considerations in studying human subjects.
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Null hypothesis testing
Null hypothesis significance testing (NHST) is a widely used statistical method in the human sciences, including the biological, biomedical, and social sciences. It is employed to investigate whether an effect is likely, even though it specifically tests for the absence of an effect or relationship. This method was popularised by Ronald Fisher, who introduced the concept in his 1935 book, "The Design of Experiments".
In the context of the human sciences, NHST can be applied in various fields. For example, in psychology, a researcher might use NHST to examine whether a new treatment for depression has a significant impact on patient outcomes. The null hypothesis would state that the treatment has no effect, and the researcher would then collect data from a sample of patients to test this hypothesis. If the data show a significant improvement in patient outcomes, the null hypothesis would be rejected in favour of the alternative hypothesis, indicating that the treatment is effective.
Similarly, in social anthropology, a researcher might utilise NHST to explore whether there is a relationship between cultural practices and economic outcomes in a particular society. By comparing data on cultural practices and economic indicators, the researcher can test the null hypothesis that there is no difference between the variables. If a significant relationship is found, the null hypothesis would be rejected, suggesting that cultural practices influence economic outcomes in the society under study.
While NHST is a valuable tool in the human sciences, it is essential to acknowledge its limitations. One criticism is that it focuses solely on the absence of an effect or relationship, potentially overlooking more nuanced relationships in the data. Additionally, the interpretation of results from NHST can be complex, as failing to reject the null hypothesis does not necessarily prove its validity but rather indicates that the data do not provide sufficient evidence to reject it.
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Frequently asked questions
Good evidence in the human sciences is evidence that is repeatable and reproducible. This means that if the same tests are carried out by different scientists, the same results should be obtained.
The human sciences, which include psychology, social and cultural anthropology, economics, and geography, differ from natural sciences in that they uncover trends rather than laws. Human scientists may refer to the world around them to check if a hypothesis is reflected in reality, but it is difficult to claim with certainty that something is a scientific fact due to the changing nature of social realities.
In the human sciences, anecdotal evidence is not as strong as a wide variety of evidence. For example, a scientist should not immediately discount evidence from 300 different ball bearings that plastic bearings are capable of doing the job of steel ball bearings in electric car windows, just because one auto mechanic reported that plastic bearings didn't hold up.
Rudolf Carnap recommended three categories to distinguish different approaches: classificatory (whether the evidence confirms the hypothesis), comparative (whether the evidence supports a first hypothesis more than an alternative hypothesis), and quantitative (the degree to which the evidence supports a hypothesis). Achinstein also identified four concepts of evidence: epistemic-situation evidence, subjective evidence, veridical evidence, and potential evidence.
