#Statisticly

The "Random Sample" is a myth. We’ve spent years accepting the hidden flaws of traditional research—moods, fatigue, and "black box" statistical summaries. Synthetic research isn't about being "perfect"; it’s about being visible. The real game-changer? ✅ Assumptions are documented, not hidden. ✅ Replicating is cheap, not expensive. ✅ Errors are visible, not buried. It’s time to stop guessing and start testing. What are you testing first?

Popper: Claims must risk refutation. Kuhn: Science shifts after anomalies. Check for patched gaps or risked cores. #PhilosophyOfScience #Falsifiability #Paradigms #ScientificClaims #ResearchMethods #ScienceFacts

Unintentional error: Avoiding common experimental artifacts (especially batch & positional effects): Although outright fraud and research misconduct definitely does occur in science sometimes, many “reproducibility” problems in science stem from poor experimental design. And some of this comes from lack of adequate training in how to set up experiments to avoid artifacts–and how to recognize them if they do occur. Some of the common ways artifacts can arise are from systematic errors including batch effects & edge effects. They get more attention (and thus have more resources for training against) in “high-throughput” (lots of samples analyzed rapidly) data collection fields (the various “-omics”) but they can also be a big problem in smaller-scale experiments such as those done in academic research labs around the world. So, here are a few tips for setting up your experiments. https://thebumblingbiochemist.com/365-days-of-science/artifacts/ & https://youtu.be/pas66G5gtmg And yes, I’m fine thanks! Tried to wave to a colleague on a scooter and my face said hi to concrete instead. Oops. “Batch effects” – differences in results (even of identical samples) run on different days, with different equipment, by different people, etc. - Seemingly small differences in temperature, humidity, technique, buffer pH, pipets, etc. can lead to inconsistent results “Edge effects” – differences in results (even of identical samples) of samples towards the middle of a multi-well plate vs on the edges - Caused by uneven heating, etc. Much more on them here: https://bit.ly/edge_effects Along similar lines, you can see differences running samples in the center of a gel vs the edges (try to avoid edges, where “smiles” and “frowns” can lead to uneven running due to uneven heating). Other sources of position-dependent error can arise from things like: – Multichannel pipets where one of the channels is “off” calibration-wise and/or the tip doesn’t like to grab well – Poor and/or inconsistent technique (e.g. pipetting with a multichannel pipet is done at an angle, so the volumes in the channels are uneven) Finished below

What if I told you that modern statistics ,the foundation of clinical trials, drug testing, and scientific research was born at a tea party? In the 1920s, a woman named Muriel Bristol claimed she could taste whether milk was poured into a cup before or after the tea. It sounds absurd. Everyone laughed. But Ronald Fisher, a scientist at the party, didn’t laugh. He designed an experiment: 8 cups of tea, 4 with milk first, 4 with tea first, presented in random order. Her task? Identify which was which. The odds of guessing all 8 correctly by chance? Just 1 in 70. She got every single one right. This experiment led Fisher to formalize the null hypothesis, the idea that you start by assuming nothing is happening (she’s just guessing), then see if the evidence is strong enough to reject that assumption. Think of it like a courtroom: innocent until proven guilty, but the data is the evidence. Today, this framework is how we test everything , from vaccines to business strategies to psychological studies. All because one man took a “ridiculous” claim seriously. #statistics #science #nullhypothesis #ronaldfisher #probability [datascience sciencehistory hypothesis experimentaldesign behavioralscience criticalthinking scientificmethod tea mathisfun ]

📍Hook Confused between parametric and non-parametric tests? Let’s make it simple. Caption Parametric tests are statistical tests that assume the data follows a normal distribution and uses parameters like mean and standard deviation. These tests are powerful when assumptions are satisfied. Common examples: • t-test • ANOVA • Pearson correlation Before applying them, always check: ✔ Normality ✔ Homogeneity of variance ✔ Interval or ratio scale data If assumptions are violated, your results may be misleading. Research is not about using fancy tests, it is about using the correct test. 📍CTA (Call to Action): Save this post for revision and follow for more research concepts made simple. 🔖#ParametricTests #Statistics #ResearchMethods #DataAnalysis #QuantitativeResearch

A small p-value does NOT mean good research Statistical significance is not the same as real-world importance. Research quality depends on design, logic, and interpretation. #Pvalue #Statistics #ResearchTips #DataAnalysis #AcademicTok #LearnWithDrYemi Comment “CLEAR” if this helped you.

🔖Hashtags #Non Parametric Tests #Research methodology #Statistics for research #Phd preparation #Data analysis 📍Prompt How do we analyze data when normal distribution assumptions are not met? 📍Caption Non-parametric tests are statistical techniques used when data do not follow a normal distribution or when sample sizes are small. These tests rely on ranks rather than raw values, making them flexible and reliable for real-world research situations where ideal conditions rarely exist. 📍Hook When your data refuses to behave normally, non-parametric tests step in to save the analysis. 📍Call to Action (CTA) Learn when and how to use non-parametric tests to make your research results valid and defensible.

Most of my followers either got this wrong or refused to answer the question. We don't accept the null hypothesis in any circumstances. We either fail to reject or retain if we cannot reject it. References below: Cohen, J. (1994). The earth is round (p < .05). American Psychologist, 49(12), 997–1003. https://doi.org/10.1037/0003-066X.49.12.997Fisher, R. A. (1935). The design of experiments. Oliver & Boyd.Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 231(694–706), 289–337. https://doi.org/10.1098/rsta.1933.0009 . . . . #statistics #research #psychology #knightofsteel

Pearson Correlation The Pearson correlation is a statistical test used to measure the strength and direction of a linear relationship between two continuous variables. Pearson correlation calculates a coefficient (r) that ranges from –1 to +1, indicating the strength and direction of the linear association. Example Suppose we measure height and weight in a group of adults. If both variables are normally distributed and a scatterplot shows a straight-line pattern, Pearson correlation is used to evaluate the relationship. If the result is r = 0.70, this indicates a strong positive linear relationship, meaning taller individuals tend to weigh more. This shows association, not causation.

This beautifully handwritten and color-coded study sheet clearly explains Type I Error (α – False Positive) and Type II Error (β – False Negative) in hypothesis testing with simple definitions, real-life examples, symbols, and an easy-to-understand decision table, making it perfect for statistics students, researchers, and exam preparation; it helps you confidently understand the difference between rejecting a true H₀ and failing to reject a false H₀ so you never mix up alpha and beta again. 📊📚 #Statistics #HypothesisTesting #TypeIError #TypeIIError #Alpha #Beta #FalsePositive #FalseNegative #StatisticsStudent #ResearchLife #StudyNotes #ExamPreparation

Systematic review doubts? Embrace counterintuitive findings! That's science—actively proving yourself wrong. Refine, probe, and find power in the unexpected. #SystematicReview #ScienceFacts #ResearchTips #CounterIntuitive #ScientificMethod #ResearchFindings #ScienceTruth

Explanations aren't evidence. A measurement that *could've* disproved my model... *didn't*. Huge difference. #ScienceExplained #ScientificMethod #CriticalThinking #EvidenceBased #ScienceFacts #RealScience