I agree but that's a pretty thorough meta analysis it's been awhile since I read the article but as I recall the primary issue is confirmation bias not in assumptions but reflected in the data. They don't have sufficient variability over a large sample size. The logical conclusion being the data is being reported in a biased manner which supports the presumed hypothesis. There is a very important distinction between producing data and choosing post hoc a theory which is familiar to you and, as the author suggests due to a lack of experimental control allowing bias, most likely without intent, to influence the data reported. Simple experimental controls where the scientists are blind to the data they are analyzing and what it represents protects the integrity and validity of research. Humans as you eloquently stated are biased by nature which is why experiments should always be designed to control for it.Chronos said:The article evokes a sense of pareidolia, regarding big data. Intelligent life forms are predisposed to make associations between the unknown and familiar. It serves a vital survival role to anticipate danger by drawing parallels between new, unfamiliar data and data from past experience. Unanticipated and/or intense sensory inputs [e.g., loud noises] are autonomically processed as potential threats. Inputs reminiscent of pleasant experiences [e.g., music] are similarly processed. You cannot divorce the observer from this kind of bias because it is a hardwired response. I prefer to think the data can speak for itself. Correlations only suggest the possibility a data set varies due to something other than random noise. It could be a known factor, an unknown factor, or merely systematic error. That is what I expect big data analysis should be capable of discerning.
Big data refers to large and complex data sets that can be analyzed computationally to reveal patterns, trends, and associations. In the context of identifying astronomical data bias, big data can be used to analyze large amounts of data from various sources and identify patterns that may suggest bias in the data.
By analyzing big data sets, researchers can identify any systematic or unintentional biases in the data, such as biases in data collection methods, instrumentation, or data processing techniques. This can help to bring awareness to potential biases and inform future data collection and analysis practices.
One challenge is the sheer volume and complexity of big data, which can make it difficult to identify and analyze patterns. Additionally, big data can be biased itself, as it may reflect societal biases and inequalities. Finally, there may also be limitations in the data itself, such as missing or incomplete data, which can affect the accuracy of the analysis.
No, while big data analysis can help to identify and address certain biases, it cannot eliminate all biases in astronomical data. Biases can exist at various stages of the data collection and analysis process, and it is important for researchers to be aware of and account for these biases when interpreting data.
By identifying and addressing bias in astronomical data, researchers can obtain more accurate and reliable data, which can lead to a better understanding of the universe. Additionally, big data can also reveal new patterns and relationships in the data, providing insights and discoveries that may have otherwise been missed.