Exploration of Empirical Misconceptions in Scientific Research

Life science is a natural science based on experiments. Over the past century, scientists have revealed the basic laws of life, such as the double helix structure of DNA, gene regulation mechanisms, protein functions, and even cellular signaling pathways, through experimental methods. However, precisely because life sciences rely heavily on experiments, it is also easy to breed “empirical errors” in research – excessive reliance or misuse of empirical data, while ignoring the necessity of theoretical construction, methodological limitations, and rigorous reasoning.Today, let’s explore several common empirical errors in life science research together:

Data is Truth: Absolute Understanding of Experimental Results

In molecular biology research, experimental data is often regarded as’ ironclad evidence ‘. Many researchers tend to directly elevate experimental results into theoretical conclusions. However, experimental results are often influenced by various factors such as experimental conditions, sample purity, detection sensitivity, and technical errors. The most common is positive contamination in fluorescence quantitative PCR. Due to the limited space and experimental conditions in most research laboratories, it is easy to cause aerosol contamination of PCR products. This often leads to contaminated samples running much lower Ct values than the actual situation during subsequent fluorescence quantitative PCR. If the incorrect experimental results are used for analysis without discrimination, it will only lead to erroneous conclusions. At the beginning of the 20th century, scientists discovered through experiments that the nucleus of the cell contains a large amount of proteins, while the DNA component is single and appears to have “little information content”. So, many people concluded that “genetic information must exist in proteins.” This was indeed a “reasonable inference” based on experience at the time. It was not until 1944 that Oswald Avery conducted a series of precise experiments that he first proved for the first time that it was DNA, not proteins, that was the true carrier of inheritance. This is known as the starting point of molecular biology. This also indicates that although life science is a natural science based on experiments, specific experiments are often limited by a series of factors such as experimental design and technical means. Relying solely on experimental results without logical deduction can easily lead scientific research astray.

Generalization: generalizing local data to universal patterns

The complexity of life phenomena determines that a single experimental result often only reflects the situation in a specific context. But many researchers tend to rashly generalize phenomena observed in a cell line, model organism, or even a set of samples or experiments to the entire human or other species. A common saying heard in the laboratory is: ‘I did well last time, but I couldn’t make it this time.’ This is the most common example of treating local data as a universal pattern. When conducting repeated experiments with multiple batches of samples from different batches, this situation is prone to occur. Researchers may think they have discovered some “universal rule”, but in reality, it is just an illusion of different experimental conditions superimposed on the data. This type of ‘technical false positive’ was very common in early gene chip research, and now it also occasionally occurs in high-throughput technologies such as single-cell sequencing.

Selective reporting: presenting only data that meets expectations

Selective data presentation is one of the most common but also dangerous empirical errors in molecular biology research. Researchers tend to ignore or downplay data that does not conform to hypotheses, and only report “successful” experimental results, thus creating a logically consistent but contrary research landscape. This is also one of the most common mistakes that people make in practical scientific research work. They pre-set expected results at the beginning of the experiment, and after the experiment is completed, they only focus on experimental results that meet expectations, and directly eliminate results that do not match expectations as “experimental errors” or “operational errors”. This selective data filtering will only lead to incorrect theoretical results. This process is mostly not intentional, but a subconscious behavior of researchers, but often leads to more serious consequences. Nobel laureate Linus Pauling once believed that high-dose vitamin C could treat cancer and “proved” this viewpoint through early experimental data. But subsequent extensive clinical trials have shown that these results are unstable and cannot be replicated. Some experiments even show that vitamin C may interfere with conventional treatment. But to this day, there are still a large number of self media outlets quoting Nas Bowling’s original experimental data to promote the so-called one-sided theory of Vc treatment for cancer, greatly affecting the normal treatment of cancer patients.

Returning to the spirit of empiricism and surpassing it

The essence of life science is a natural science based on experiments. Experiments should be used as a tool for theoretical verification, rather than a logical core for replacing theoretical deduction. The emergence of empirical errors often stems from researchers’ blind faith in experimental data and insufficient reflection on theoretical thinking and methodology.
Experiment is the only criterion for judging the authenticity of a theory, but it cannot replace theoretical thinking. The progress of scientific research relies not only on the accumulation of data, but also on rational guidance and clear logic. In the rapidly developing field of molecular biology, only by continuously improving the rigor of experimental design, systematic analysis, and critical thinking can we avoid falling into the trap of empiricism and move towards true scientific insight.