IB›IB Maths: Analysis and Approaches SL›Mind maps4.1 Populations, samples and data collectionIB Maths: Analysis and Approaches SL: Mind mapStudy pack PDFAlso for this subtopic:Revision notesFlashcardsSubtopic testCover factsKey termsPopulation: the whole group studiedSample: the part you collect data fromRandom: every member has an equal chanceDiscrete data is counted, continuous is measuredAge is continuous, even if written as whole yearsSampling methodsSimple random: number everyone, use random numbersSystematic: every kkkth member after a random startStratified: random from each group, in proportionQuota: fixed numbers per group, chosen non-randomlyConvenience: whoever is easiest to reachStratified sumsNumber from a stratum = stratumpopulation×sample\dfrac{\text{stratum}}{\text{population}} \times \text{sample}populationstratum×sample330 DP1 and 270 DP2, sample of 40330600×40=22\frac{330}{600} \times 40 = 22600330×40=22 from DP1Then 18 from DP2Samplingpopulations and dataQ1Q_1Q1Q3Q_3Q3BiasBiased: systematically over or under-represents some groupSay who is left out and why it mattersSystematic sampling fails if the list has a patternStratified helps when groups differ on the variableOutliersBelow Q1−1.5×IQRQ_1 - 1.5 \times \text{IQR}Q1−1.5×IQRAbove Q3+1.5×IQRQ_3 + 1.5 \times \text{IQR}Q3+1.5×IQRQ1=220Q_1 = 220Q1=220, Q3=260Q_3 = 260Q3=260: outliers under 160 or over 320Genuine extremes are kept; errors are fixed or removedMissing data is excluded, never set to 0Exam tipsStratified is random within groups; quota is notLink every comment to the contextNever remove an outlier without a reasonCheck stratified totals still equal the sample size