My Class Reading scientific graphs & data (full guide)The illustrated deep-dive: graph anatomy, variables, every graph type, best-fit lines, error bars (SD, SE, CI), slope, and correlation vs causation.
📐
Applied Mathematics for Science
The math you actually use to do science: units, graphs, and data.
The math that shows up in a biomedical lab is not abstract: it is converting a dose, reading a graph, and telling a real effect from noise. This hub breaks each skill into a prerequisite, a re-teach with worked practice, and an advanced extension, with example data, an illustrated glossary, vetted videos, and downloadable guided notes.
Three levels
Prerequisite, remediation, and advanced extension for every skill.
Worked practice
Vetted questions with step-by-step solutions and example data.
Study-ready
Illustrated glossary, vetted videos, and downloadable guided notes.
Numbers, Units & Conversions
· 4 topicsCoreQuantitative reasoning: unit conversionFactor-Label Conversions (Dimensional Analysis)Turn any measurement into the units you need by multiplying by fractions that each equal one.CoreQuantitative reasoning: fractions & ratiosFractions, Ratios & Proportions in ScienceSee a fraction as both a part of a whole and a division, then use ratios and proportions to solve dilutions, doses, crosses, and scale problems.CoreQuantitative reasoning: percentagesPercentages, Percent Change & Percent ErrorRead a percent as 'per 100', find a percent of a number, and measure how much a value changed or how far off a measurement was.CoreQuantitative reasoning: units & notationScientific Units & Scientific NotationLearn the standard units science uses, what metric prefixes mean, and how to write very large and very small biology numbers cleanly with powers of ten.
Designing & Modeling Investigations
· 3 topicsCoreExperimental design: controlsControl Groups & Controlled ExperimentsSet up a fair test by comparing a treated group against an untreated control while every other variable is held the same.CoreModeling: graphs as modelsGraphs as ModelsRead a graph as a working model of how a system behaves, then use a line of best fit to predict inside the data (interpolation) and, more carefully, beyond it (extrapolation).CoreExperimental design (variables)Independent vs Dependent VariablesTell apart the one thing you change on purpose from the thing you measure in response, and place each on the correct axis.
Graphing: Build, Choose, Read
· 2 topicsCoreData representation: graph choiceChoosing the Right Graph (Every Type & When to Use It)Match your data to the one graph that shows its story clearly, from bar and line to scatter, histogram, box plot, pie, dual-axis, and log scale.CoreData analysis: reading graphsReading & Analyzing Graphs (Trends, Correlation vs Causation)Read the axes and scale first, describe the trend, and tell what a graph implies apart from what it actually proves.
Complex Biological Data
· 3 topicsCoreBiological data: allele frequenciesPopulation Allele-Frequency Charts (Hardy-Weinberg)Read a bar chart of allele or genotype frequencies, use p + q = 1, and predict genotype proportions with p^2 + 2pq + q^2 = 1.CoreBiological data: DNA sequencingReading DNA Sequencing Charts (Chromatograms)Read a DNA sequence base by base from a Sanger chromatogram, and spot a heterozygous site where two colored peaks overlap at one position.CoreBiological data: genetic driftGenetic Drift: Small vs Large PopulationsRead allele-frequency graphs to see why chance alone changes gene frequencies faster in small populations than in large ones.
Statistics & Data Analysis
· 7 topicsCoreStatistics: correlation & regressionCorrelation & Line of Best FitRead a scatter plot, describe how two measured variables move together (direction and strength), and use a line of best fit to predict without confusing correlation with cause.CoreStatistics: measures of centerMean, Median, Mode & RangeSummarize a set of biology measurements with one number for the center (mean, median, or mode) and one for the spread (range), and know when each is the honest choice.CoreStatistics: distributionsDistributions & the Normal CurveSee the shape of your data: the symmetric bell curve, skewed shapes, the 68-95-99.7 rule, and how to read where one value falls.CoreStatistics: probabilityProbability in BiologyProbability is the chance an event happens, written as a number from 0 to 1 (or 0% to 100%). Learn to count outcomes, multiply independent events, and predict genetic crosses.CoreStatistics: sampling & confidenceSamples, Error Bars & ConfidenceA sample is a small window on a whole population. Learn why samples wobble, how error bars show that wobble, and when two groups really differ.CoreStatistics: significance testingIs the Difference Real? Significance & p-valuesDecide whether a difference between groups is a real effect or just the luck of the draw, using the p-value and the right test.CoreStatistics: measures of spreadStandard Deviation & SpreadTwo data sets can share the same average yet look nothing alike. Spread tells you how scattered the values are, and standard deviation puts a single number on it.