Natural Products PhD candidate | Metabolomics and psychedelics researcher | Enthusiast of decentralized financial systems, passionate about nature and science
@Pdorrestein1@realBioMassSpec Owww @Pdorrestein1 we are so honored you liked our work. It was a long journey, but I’m so happy that its finally public. Also i must say u are a true inspiration for myself!
And I wasn’t expecting a comment from you here
Made my day ahahahaah
@mthferalv
We are so glad to share this teamwork research with the community! Definitely now we can build useful molecular networking of DIA data using a open mass spec format for integrative metabolomics studies!! 🙏🤩
A Gentle Introduction to Bootstrapping
1/🥾 Intro to Bootstrapping:
Imagine trying to measure the size of fish in a pond. Instead of measuring every fish (impractical!), you catch some, measure, and release. Now, to better understand fish sizes without catching more, you repeatedly "resample" from your caught fish and analyze these samples. This method is a lot like "bootstrapping" in statistics!
2/🎲 The Power of Resampling:
Bootstrapping is all about taking many "resamples" from your original data and recalculating your desired statistic (like a mean or median). By doing this thousands of times, you get a distribution of that statistic, allowing you to make more informed decisions and inferences.
3/💼 Why Bootstrap?:
Real-world datasets can be tricky - they might be small, have outliers, or you might just be unsure about making strong assumptions about their distribution. Bootstrapping is a tool that can help, as it doesn't rely heavily on assumptions about the shape or type of our data distribution.
4/🔁 Steps to Bootstrap:
a. Draw a random sample from your data WITH replacement.
b. Compute the statistic of interest.
c. Repeat steps a & b many times (like 10,000!).
d. Examine the distribution of your statistic across all bootstrap samples.
5/📊 Bootstrap Confidence Intervals:
One of the most popular uses of bootstrapping is to create confidence intervals. By looking at the distribution of your bootstrapped statistics, you can determine intervals where, for example, the median value of your data is likely to fall 95% of the time.
6/⚠️ When Not To Bootstrap:
While powerful, bootstrapping isn't always the answer. If your original sample isn't representative of the population, bootstrapping won't fix it. It can also be computationally intense and might not always work well with very complex statistics.
7/🌐 Modern Computing & Bootstrapping:
The power of modern computers has made bootstrapping more accessible than ever. Software like R and Python has built-in tools to help you bootstrap with ease, allowing for more robust statistical analyses.
8/🔍 Final Thoughts:
Bootstrapping, at its core, harnesses the power of resampling to provide clearer insights into the properties of your data. It's like getting multiple viewpoints on a subject by just shifting your perspective!
Found this thread on bootstrapping helpful? Dive deeper, ask questions, or share your own experiences with bootstrapping below! Remember, statistics is all about learning and iterating. Like, retweet, and comment to keep the conversation going!👇
#Statistics #DataScience
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