C127 Lecture 5 Notes- Density and Data Analysis 2-11-16 ∙ Learning goals
o Learn how to design an experiment to give best possible results
o Learn how to use standard deviation to examine quality of data sets
o Learn how to use t-test in Excel to decide if 2 experiments gave same or different results ∙ Safety/Waste Disposal
o Goggles and appropriate clothing required
o All solutions can be put down sink with plenty of water
∙ �������������� (������) =�������� We also discuss several other topics like Migration, is what?
∙ Lab Info
o 2 solutions with different amounts ��������
o Measure 2 unknowns, use t-test to make hypothesis to say whether solutions are the same density o For this lab, density concept not very important; focusing on analyzing data sets
o To get accurate data we need: Don't forget about the age old question of What is the popular sovereignty?
▪ Accurate mass- analytical balances (with doors on sides) go to 4 decimal places- keep in mind they have mass limit
▪ Accurate volume- choose glassware based on what is available/best-suited
∙ Beakers- mass produced; measurements very very approximate; never measure with beakers ∙ Same goes for Erlenmeyer flasks
∙ Graduated cylinder- not best overall choice (not calibrated), but good; pick one closest in size to amount of liquid you need
If you want to learn more check out What is a tony award and where did it get its name?
∙ Volumetric pipets/flasks- best choices for accurate and reproducible volume; made to be accurate and precise through individual calibration; known as Class A glassware
∙ Quality of Data
o All data has error
o Exclude data if there is some major mistake; repeat step(s) to replace bad data
o Random error- equally likely to give high result as low; cannot get rid of it
o Systematic error- always makes data high or always makes it low; cannot identify unless you know what a value should be (ie. theoretical yield, you spilled something); can be reduced as much as possible o ALWAYS ON EXAMS- random error always present; will ask about differences between two kinds and how to know if systematic is present Don't forget about the age old question of How did stalin secure single-party rule for bolshevik party?
We also discuss several other topics like What defines a function?
Don't forget about the age old question of Is a higher or lower roe better?
o Percent error- when true value known:
▪ % ���������� = |��ℎ������������������−������������������������
�������� ���������� | ∙ 100
o When true value not known, use statistics
▪ Standard deviation �� = √∑(����−��̅)2
▪▪ When scale on axis based on numbers (not ��), wider curve = more random error, narrower curve = less; always looks same based on ��
▪ Our data sets will be much smaller; not a nice curve
▪ For us, justified to throw out data beyond ±2�� due to small sample size instead of typical ±3�� o How different do two limited sets of data need to be before we can safely say they are different? (use T-test)