Exam 1 Lecture 2 Flashcards

1
Q

what does statistics give us tools to do?

A
  • accept conclusions that have a high probability of being correct
  • reject conclusions that have a high probability of being incorrect
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2
Q

Gaussian Distribution (AKA normal distribution)

A

if an experiment is repeated a great many times and if the errors are purely random:
- the results tend to cluster symmetrically about the average value
- the more times the experiment is repeated, the more closely the results approach a Gaussian distribution

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3
Q

the smaller the standard deviation, s, the more closely the data…?

A

are clustered about the mean (high precision)
greater precision does not necessarily imply greater accuracy!!

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4
Q

standard deviation (s)

A

measures how closely data are clustered about the mean

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5
Q

relative standard deviation (RSD)

A

standard deviation expressed as a percentage of the mean

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6
Q

variance

A

s^2

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7
Q

in a gaussian curve, the sum of the probabilities of all measurements must be what?

A

in unity; the probability of observing a rule within a certain interval is proportional to the area of that interval

the area under the whole curve from z= negative infinity to positive infinity adds up to 1.

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8
Q

what does the standard deviation measure (Gaussian curve)

A

the width of the Gaussian curve

the large the sd, the broader the curve

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9
Q

the more times a quantity is measured…

A

the more confident you can be that the mean is close to the population mean

  • uncertainty decreases in proportion to 1/srt(n)
  • u=s/srt(n) where u measures the uncertainty in the mean (x), u reaches 0 as n approaches infinity and s measures uncertainty in x, s approaches constant value as n approaches infinity
  • you can decrease the uncertainty by a factor of 2 by making 4 times as many measurements
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10
Q

F-test

A

used to compare two variances (s^2 values)
- Fcalculated = s^2/s^2 where the first variance on top is greater than the variance on the bottom
- “are the mean values of two sets of measurements statistically different from each other when experimental uncertainty is considered?”

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11
Q

null hypothesis

A

states that two sets of data are drawn from populations with the same properties
- observed differences arise only from random variation in measurements
- reject the null hypothesis if there is less than 5% probability of observing experimental results from two populations with the same value

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12
Q

when should you reject the null hypothesis (F-test)

A

if Fcalculated>Ftable

  • there is <5% chance that the two data sets came from populations with the same population standard deviation
  • the difference is considered significant
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13
Q

Student’s t

A

statistical tool used to find confidence intervals and is also used to compare mean values measured by different methods; used to compare results from different experiments

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14
Q

confidence intervals

A

confidence interval= x+- ts/srt(n)

  • if we were to repeat n measurements many times, the 95% confidence interval would include the true population mean (whose value we do not know) in 95% of the sets of n measurements
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15
Q

t Test (comparison of means)

A

determines if there is a statistical difference between x1 and x2

  • if you make two sets of measurements of the same quantity, generally x1 does not equal x2 due to random variations in measurements
  • “Are the means of two sets of measurements statistically different?”
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16
Q

when should you reject the null hypothesis (t Test)?

A

if tcalculated > ttable

  • there is a <5% chance that the two data sets came from populations with the same population mean; the difference is considered significant
17
Q

Grubbs Test for Outliers

A

if Gcalculated > Gtable, the questionable point should be discarded; only one outlier may be rejected using the Grubbs test

Gcalculated= (questionable value - x)/ standard error