Karlo Melendres

Ophthalmologist. Neuro-ophthalmologist.CHESS ADDICT. Husband and a father of 1 cute daughter

[Charlie @SuccessBluePrtX]

Steal These 50 Claude AI Prompts.

For creators who want to work smarter in 2026.

- Thread - 📌

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Every data point has a rank. Turn that rank into a quantile, compare it with a reference distribution, and you get a powerful diagnostic tool: the Q–Q plot 📈
If points follow the line, your data matches the reference.
#Statistics #DataScience #QQPlot #Quantiles #ResearchMethods

[Hamidullah Khan, PhD @IamKhanPhD]

🚫 STOP telling Claude

"Write like a Human"
“Humanise this"
“Humanise that"

Bad prompting = Bad writing

Use this insane workflow instead & see the Magic👇

(📌 Save this before you write your next AI-assisted post)

[Juanlu_CabanillasGar @JuanluCaba_Unex]

📊 Correlation Matrices: Your Data’s Relationship Map!
A correlation matrix reveals how variables move together, with values ranging from -1 to +1. Positive values indicate variables move in the same direction, while negative values show inverse relationships.

[Hamidullah Khan, PhD @IamKhanPhD]

🚨 Stop spending hours creating classroom materials from scratch.

Claude can build worksheets, quizzes, reading tasks, discussion questions, graphic organizers, and revision resources in minutes.

Here are Claude prompts every teacher should save 👇

(Save this for your next lesson-planning session)

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Sample size is not one-size-fits-all.
The right formula depends on your population, study design, and analysis plan. A good sample size improves the validity, precision, and impact of your research.
✅ Don't choose the formula first—understand the study first. #SampleSize

[Juanlu_CabanillasGar @JuanluCaba_Unex]

🎯 Choosing the correct probability distribution is crucial for statistical modeling.
• Bernoulli → binary outcomes
• Binomial → number of successes in n trials
• Poisson → event counts
• Exponential → time between events
• Normal → natural variability around a mean.

[QDくん⚡️AI関連の無料教材紹介 @developer_quant]

米国のデータサイエンティストら執筆の良書
"OpenIntro Statistics" の和訳版
『データ分析のための統計学入門』
は pdf (400ページ超) が無料公開されている。
具体例や練習問題が豊富でわかりやすい。
https://t.co/MsTe23FoPL

和訳版は練習問題の解答が省略されているが、
原著には奇数番の解答がある。
(以下リンクで"The Book"を選ぶと無料でDLできる)
https://t.co/2a7h21kLjd

1章　データ分析への誘い
2章　統計データの記述
3章　確率
4章　確率変数の分布
5章　統計的推測の基本
6章　カテゴリカル・データの統計的推測
7章　量的データに対する推測
8章　線形回帰への入門
9章　重回帰とロジスティック回帰

[tut_ml @tut_ml]

Best Statistics Courses- https://t.co/kxPBP3nq1O

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Before claiming that one treatment, method, or strategy outperforms another, researchers often use ANOVA. By comparing variation across multiple groups, ANOVA provides a rigorous framework for testing scientific hypotheses.
📈 Data → Evidence → Knowledge
#Statistics #Science

[Hamidullah Khan, PhD @IamKhanPhD]

DELETE UDEMY.
DELETE COURSERA.
DELETE SKILLSHARE.

Use Claude to build your own personalized online courses, quizzes, projects, and study plan.

Here are 08 Claude Prompts that can replace paid courses 👇👇

(📌 One save can replace your next paid course)

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Why do some studies produce misleading results? 🤔
The answer may lie in extraneous variables: hidden factors like sleep, stress, noise, or temperature that influence outcomes without being the focus of the study.
Good research doesn't just measure effects. It controls the noise.

[Juanlu_CabanillasGar @JuanluCaba_Unex]

📊 Comparing two groups? The difference between two sample means has its own sampling distribution.
✅ Center = μ₁ − μ₂
✅ Variance = σ₁²/n₁ + σ₂²/n₂
✅ Approximately Normal (for independent samples)
A simple rule to remember: subtract the averages, add the uncertainty.

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Ever spent more time choosing a statistical test than analyzing your data? 😅 Here's a practical roadmap to help you navigate t-tests, ANOVA, correlations, regressions, and more. Save it for your next research project! 📈🔬 #AcademicTwitter #Stats #ResearchMethods

[Juanlu_CabanillasGar @JuanluCaba_Unex]

🎓 Three teaching methods. 📈 Three different average scores.
But are the differences real or just random variation?
ANOVA provides the answer by testing whether at least one group mean differs significantly from the others.
Statistics turns data into evidence. 🔍
#ANOVA #Science

[Juanlu_CabanillasGar @JuanluCaba_Unex]

📚 Before running any statistical analysis, classify your data correctly.
A solid understanding of data types is the foundation of rigorous research and reliable conclusions. #Statistics #Research #DataAnalysis #AcademicTwitter

[Steven @stevencoder9]

How To Never Hit Claude’s Limits

[Emmanuel Tsekleves @PhDtoProf]

I have supervised 25+ PhD theses. Four habits break them.

The fourth one was mine.

[Juanlu_CabanillasGar @JuanluCaba_Unex]

Your research deserves a title that gets noticed. ✨
From literature exploration to journal fit, AI tools can help researchers craft clear, novel, and impactful article titles.
Which tool do you use most? 👇
#ResearchTips #ArtificialIntelligence #Academia #HigherEd

[Math Files @Math_files]

The Fibonacci sequence can be used to quickly estimate miles in kilometers.

The sequence is formed by adding the previous two numbers:

1, 1, 2, 3, 5, 8, 13, 21, 34, …

For example:

5 miles ≈ 8 kilometers
8 miles ≈ 13 kilometers
13 miles ≈ 21 kilometers
21 miles ≈ 34 kilometers

This works because consecutive Fibonacci numbers have a ratio close to the golden ratio, about 1.618. The conversion factor between miles and kilometers is about 1.609, which is very close. Therefore, Fibonacci numbers give a convenient approximation for converting between miles and kilometers.