Here is a rewritten version of the text in a more readable format:

**The Problem with Medical Research: Bernoulli's Fallacy**

As someone who has spent years studying and working in medical research, I have come to realize that there is a fundamental problem with the way our field approaches data analysis. This problem is known as Bernoulli's Fallacy, named after the 18th century Swiss mathematician Jacob Bernoulli.

**What is Bernoulli's Fallacy?**

Bernoulli's Fallacy refers to the mistake of assuming that correlation implies causation. In other words, just because two things are related, it doesn't mean that one causes the other. For example, consider a study that finds a correlation between smoking and lung cancer. Just because smokers have a higher risk of developing lung cancer, it doesn't mean that smoking directly causes lung cancer.

**The Problem with Biostatistics**

In modern medicine, we often rely on biostatistics to analyze data. However, biostatistics is not a rigorous discipline based on proof like mathematical statistics. Instead, it is more concerned with finding patterns and correlations in data, even if those patterns are not necessarily causal.

This approach can lead to flawed conclusions and bad outcomes. For example, the Hormone Replacement Therapy (HRT) disaster of the 1990s showed that relying too heavily on biostatistics can have disastrous consequences. Researchers found a correlation between HRT and an increased risk of heart disease, but they failed to consider other factors that may have contributed to this outcome.

**The Consequences**

The problem with Bernoulli's Fallacy is not just academic; it has real-world consequences. In the case of COVID-19, bad science and ruthless marketing led to billions of dollars going straight into Big Pharma's pockets while potentially creating millions of cripples around the world for a non-solution to a non-problem.

**The Solution**

So what can we do to address this problem? The first step is to understand Bernoulli's Fallacy and the difference between statistical significance and causation. We need to recognize that correlation does not necessarily imply causation, and that data analysis requires a much more nuanced approach.

By understanding the limitations of biostatistics and the importance of rigorous mathematical statistics, we can begin to develop new approaches to data analysis that prioritize accuracy and truth over simplistic patterns and correlations.

**Conclusion**

As someone who has spent years studying and working in medical research, I am disheartened by the state of our field. However, I remain hopeful that by acknowledging the problem of Bernoulli's Fallacy, we can begin to develop new approaches to data analysis that prioritize accuracy and truth over simplistic patterns and correlations.

It is time for us to rethink our approach to medical research and to demand more rigor and attention to detail from ourselves and our colleagues. Only then can we hope to create a safer, healthier world for everyone.