Are There Unsolved Problems in Statistics?

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In summary, the conversation touches upon the observation that most major problems in math and statistics seem to occur only in math. This could be due to statistics being a relatively new field, with its origins traced back to Florence Nightingale in the early 1900s. It is also suggested that statisticians primarily work with concrete and experimental data, compared to pure math. The equivalent of the Riemann hypothesis in statistics is unknown, though there are unsolved problems in the field. It is noted that one can obtain a Ph.D. in statistics and there is ongoing research in the field, including on topics such as Brownian motion.
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fourier jr
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it seems like all the major problems in math/stats are only in math. why is that? is it because stats is a relatively new field of study (got started ~50yrs ago i think by florence nightingale?), not counting gauss' central limit theorem? or is it because statisticians only work with data collected in experiments, making it extremely concrete/experimental compared with math (esp pure math)? (that might come across as more ignorant that i want it to :blushing: ) what's the equivalent of the riemann hypothesis in statistics, if there is one?
 
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Florence Nightingale died in the early 1900s, she was born in the early 1800s.

Apart from that I have little to offer in this.
 
  • #3
Also, she had a pet owl.
 
  • #4
You can get a Ph.D. in statistics, so there must be some research in the field

Here's a link to the University of Minnesota's Statistics Department. There are links to faculty research areas from here

http://www.stat.umn.edu/Research/Profile.html
 
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  • #5
Why not do research on brownian motion?
 
  • #6
Statistics does not necessarily always work with data. There are unsolved problems in statistics. If there weren't, the stock market would not exist, for example.
 

1. Why are there no unsolved problems in statistics?

There are no unsolved problems in statistics because it is a well-established and mature field of study. Many researchers have dedicated their careers to advancing statistical theory and methods, resulting in a comprehensive understanding of the subject. Additionally, statistical methods are constantly being refined and updated, making it unlikely for any problem to remain unsolved for long.

2. Are there any ongoing debates or controversies in statistics?

While there may not be any unsolved problems, there are ongoing debates and controversies in statistics. These often stem from different interpretations or applications of statistical methods, as well as disagreements on the validity of certain assumptions or techniques. However, these debates are a natural part of the scientific process and help to drive progress in the field.

3. How do statisticians ensure the validity and reliability of their findings?

Statisticians use a variety of methods to ensure the validity and reliability of their findings. This includes carefully designing experiments or studies, selecting appropriate statistical techniques, and conducting thorough data analysis. Additionally, peer review and replication of results by other researchers also help to validate statistical findings.

4. Can statistics be used to prove causation?

No, statistics cannot be used to prove causation. Correlation does not imply causation, and even significant statistical relationships do not necessarily indicate a causal relationship. While statistical methods can provide evidence for a causal relationship, it is ultimately up to the researcher to interpret the results and consider other factors before making any claims of causation.

5. How has the use of technology affected the field of statistics?

The use of technology has greatly impacted the field of statistics, making it easier and faster to collect, analyze, and interpret data. With the advent of powerful statistical software and computing tools, statisticians can now handle larger and more complex datasets, and perform more sophisticated analyses. This has led to significant advancements in the field, as well as a wider range of applications for statistical methods.

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