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# Lab 0 - Statistics and the Student t-test | ||
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We use statistics to draw conclusions from limited data. No measurement is exact. Every measurement you make has two types of uncertainties, *systematic* and *random*. *Systematic* uncertainties come from faults in your assumptions or equipment. | ||
*Random* uncertainties are associated with unpredictable (or unforeseen at the | ||
time) experimental conditions. These can also be due to simplifications of your | ||
model. Here are some examples for caliper measurements: | ||
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In theory, all uncertainies could be accounted for by factoring in all physics | ||
in your readings. In reality, there is a diminishing return on investment | ||
for this practice. So we use some statistical insights to draw conclusions. |
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import matplotlib.pyplot as plt | ||
plt.rcParams.update({'font.size': 22}) | ||
plt.rcParams['lines.linewidth'] = 3 | ||
plt.rcParams['text.usetex'] = True | ||
plt.rcParams['text.latex.unicode'] = True | ||
plt.rcParams['text.latex.preamble'] = [ | ||
r'\usepackage{siunitx}', # I need upright \micro symbols, but you need... | ||
r'\sisetup{detect-all}', # ...this to force siunitx to actually use your fonts | ||
r'\usepackage{helvet}', # set the normal font here | ||
r'\usepackage{sansmath}', # load up the sansmath so that math -> helvet | ||
r'\sansmath' # <- tricky! -- gotta actually tell tex to use! | ||
] |
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