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@openstem · Joined Jul 2026
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Note~479 words · 2 min
Pick the shape before you start writing Most stuck proofs are stuck because the wrong technique was chosen in the first thirty seconds. The statement's form usually tells you which one to reach for. Direct proof Assume the hypothesis, walk
Mathematics

Mathematics · L4 · Mathematical proof techniques

@openstem
Mathematics · L4 · Mathematical proof techniques
Note~424 words · 2 min
The most-used and most-misread number in statistics. Here's what it actually says. Definition The p-value is the probability of a test statistic at least this extreme, assuming H₀ is true. That z is a position on the curve below. Slide the
Mathematics

Mathematics · L4 · p-values and statistical significance: what they really mean

@openstem
Mathematics · L4 · p-values and statistical significance: what they really mean
Note~395 words · 2 min
One formula, and what each piece is called The denominator comes from the law of total probability: P(E) = ΣP(E|Hᵢ)P(Hᵢ). It's the normalising sum over every hypothesis you're considering. The medical test that catches everyone out Updating
Mathematics

Mathematics · L4 · Bayes' theorem: updating beliefs with evidence

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Mathematics · L4 · Bayes' theorem: updating beliefs with evidence
Note~385 words · 2 min
The right chart makes the finding obvious. The wrong one hides it, or worse, invents one. Start from your data type and your question, not from the chart you feel like drawing. Distribution of a single variable Relationship between two vari
Mathematics

Mathematics · L4 · Data visualisation: choosing the right chart

@openstem
Mathematics · L4 · Data visualisation: choosing the right chart
Note~455 words · 2 min
A differential equation ties a function to its own rate of change. Separable equations An ODE is separable if it can be written . Move all the y-terms to one side and all the x-terms to the other, then integrate both sides independently: W
Mathematics

Mathematics · L4 · Solving first-order differential equations

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Mathematics · L4 · Solving first-order differential equations
Note~392 words · 2 min
The derivative becomes a vector In one variable, the derivative is a number. In several, there's a rate of change in every direction, so "the derivative" becomes a whole vector of partials. Geometrically: slice the surface z = f(x,y) with t
Mathematics

Mathematics · L4 · Partial derivatives, gradients, and multivariable optimization

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Mathematics · L4 · Partial derivatives, gradients, and multivariable optimization
Note~306 words · 2 min
Four axioms, and that's the entire definition A group is a set G with one binary operation ·. Four requirements, nothing else. Add commutativity (a·b = b·a) and the group is called abelian. Notice it isn't one of the four. The first example
Mathematics

Mathematics · L5 · Groups: Axioms and First Examples

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Mathematics · L5 · Groups: Axioms and First Examples
Note~349 words · 2 min
What a σ-algebra is for You can't consistently assign a length to every subset of ℝ. A σ-algebra is the collection of sets you agree to measure, chosen so the operations you need stay inside it. The pair (X, ℱ) is a measurable space. A meas
Mathematics

Mathematics · L5 · Measure & the Lebesgue Integral

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Mathematics · L5 · Measure & the Lebesgue Integral
Note~376 words · 2 min
Probability is measure theory with one extra axiom A probability space is a triple (Ω, ℱ, P). Everything else is bookkeeping on top of it. That P(Ω) = 1 is the only difference from a general measure space. Everything from measure theory car
Mathematics

Mathematics · L5 · Measure-Theoretic Probability: Foundations

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Mathematics · L5 · Measure-Theoretic Probability: Foundations
Note~349 words · 2 min
The problem MCMC solves You know the posterior π(θ|data) only up to a normalising constant, because the denominator is an integral you can't do. Direct sampling is out. So build a Markov chain whose stationary distribution is π, run it, and
Mathematics

Mathematics · L5 · Bayesian Computation: MCMC

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Mathematics · L5 · Bayesian Computation: MCMC
Note~357 words · 2 min
Potential outcomes Every unit i has two outcomes waiting: Y_i(1) if treated, Y_i(0) if not. The individual effect is their difference. What you can recover is an average, because averages only need each unit to contribute one arm. Which ave
Mathematics

Mathematics · L5 · Causal Inference: Potential Outcomes

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Mathematics · L5 · Causal Inference: Potential Outcomes
Note~454 words · 2 min
Throwing away distance Topology asks what survives if you keep only the notion of "open" and discard distance entirely. The answer is nearly everything that matters: continuity, connectedness, compactness. The three axioms A topology τ on a
Mathematics

Mathematics · L5 · Point-Set Topology: Open Sets, Continuity, and Compactness

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Mathematics · L5 · Point-Set Topology: Open Sets, Continuity, and Compactness
Note~447 words · 2 min
One extra requirement, enormous consequences Complex differentiability demands the limit be the same no matter which direction h approaches 0 from. In ℝ there are only two directions. In ℂ there are infinitely many, and forcing them all to
Mathematics

Mathematics · L5 · Complex Analysis: Holomorphic Functions and Cauchy's Theorem

@openstem
Mathematics · L5 · Complex Analysis: Holomorphic Functions and Cauchy's Theorem
Note~378 words · 2 min
The property that makes optimisation tractable Convexity is the line between problems you can solve and problems that are, in general, hopeless. In a convex problem the local view IS the global view, which is exactly what lets a gradient me
Mathematics

Mathematics · L5 · Convex Optimization: Convexity, Duality, and KKT Conditions

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Mathematics · L5 · Convex Optimization: Convexity, Duality, and KKT Conditions
Plot1 plot · 1 param
Mathematics

Mathematics · L2 · Proportions Are Straight Lines

@openstem
Mathematics · L2 · Proportions Are Straight Lines
PlotPlot
Mathematics

Mathematics · L2 · Reading a Scatter Plot

@openstem
Mathematics · L2 · Reading a Scatter Plot
Plot1 plot · 2 params
Mathematics

Mathematics · L3 · Slope & Intercept: y = m·x + b

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Mathematics · L3 · Slope & Intercept: y = m·x + b
Plot1 plot · 3 params
Mathematics

Mathematics · L3 · The Parabola: y = a·x² + b·x + c

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Mathematics · L3 · The Parabola: y = a·x² + b·x + c
Plot3 segments
Mathematics

Mathematics · L3 · A Right Triangle on the Grid

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Mathematics · L3 · A Right Triangle on the Grid
Plot1 plot · 2 params
Mathematics

Mathematics · L3 · The Sine Wave: y = a·sin(b·x)

@openstem
Mathematics · L3 · The Sine Wave: y = a·sin(b·x)
Plot1 plot · 2 params
Mathematics

Mathematics · L3 · The Cosine Wave: y = a·cos(b·x)

@openstem
Mathematics · L3 · The Cosine Wave: y = a·cos(b·x)
Plot1 plot · 1 param
Mathematics

Mathematics · L3 · Exponential Growth: y = b^x

@openstem
Mathematics · L3 · Exponential Growth: y = b^x
Plot1 plot · 1 param
Mathematics

Mathematics · L3 · The Logarithm: y = a·ln(x)

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Mathematics · L3 · The Logarithm: y = a·ln(x)
Plot1 plot · 2 params
Mathematics

Normal Distribution: exp(−(x−μ)²/(2σ²))

@openstem
Normal Distribution: exp(−(x−μ)²/(2σ²))
Plot1 plot · 2 params
Mathematics

Linear Regression: y = m·x + b

@openstem
Linear Regression: y = m·x + b
Plot1 plot · 1 param
Mathematics

Exponential Decay: y = e^(−k·x)

@openstem
Exponential Decay: y = e^(−k·x)
Plot1 plot · 1 param
Mathematics

CDF Growth: y = 1 − e^(−k·x)

@openstem
CDF Growth: y = 1 − e^(−k·x)
Plot1 plot · 1 param
Mathematics

Quadratic Fit: y = a·x²

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Quadratic Fit: y = a·x²
Plot1 plot · 2 params
Mathematics

Mathematics · L5 · Normal Density: shift the mean μ and scale σ

@openstem
Mathematics · L5 · Normal Density: shift the mean μ and scale σ
Flowchart15 objects
Mathematics

Mathematics · L3 · Absolute Value: Equation vs '<' vs '>'

@openstem
Mathematics · L3 · Absolute Value: Equation vs '<' vs '>'

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