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Chapter 4 Statistics Tools to accept or reject conclusion from experimental measurements Deal with random error only.

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Presentation on theme: "Chapter 4 Statistics Tools to accept or reject conclusion from experimental measurements Deal with random error only."— Presentation transcript:

1 Chapter 4 Statistics Tools to accept or reject conclusion from experimental measurements Deal with random error only

2 Is my red blood cell count high today? P82

3 4-1 The Gaussian Distributions -1 1) Nerve cells muscle cells (1991 Nobel Prize in Medicine & Physiology) Sakmann & Neher absence neurotransmitter present neurotransmitter P83.

4 4-1 The Gaussian Distributions -2 922 ion channels response Typical lab measurements: Gaussian distribution P84

5 4-1 The Gaussian Distributions -3 Gaussian distribution is characterized by 1)Mean: 2)Standard deviation: P84     1n xx S σS xxx n 1 n x x μx i 2 i n21 i i        

6 4-1 The Gaussian Distributions -5 The smaller the s,  the more precise the results  reproducible P85

7 4-1 The Gaussian Distributions -4 Other terms Median Range σ & probability Table 4.1 P86

8 4.2 F test F calculated = (S 2 1 /S 2 2 )

9 TABLE 4.3

10 4-3 Student’s t Student’s t is the statistical tool used to express confidence intervals & to compare results from different experiments. confidence interval : allows us to estimate the range within which the true value (  ) might fall, (given probability = confidence level) defined by mean and standard deviation. P86

11 Calculating confidence intervals (ex) In replicate analyses, the carbohydrate content of a glycoprotein (a protein with sugars attached to it) is found to be 12.6, 11.9, 13.0, 12.7, and 12.5 g of carbohydrate per 100 g of protein. Find the 50 % and 90% confidence intervals for the carbohydrate content. P87

12 P86

13 P87

14 Improving the reliability of your measurements Smaller confidence intervals Better measurement For 90% sure that a quantity lies in the range 62.3  0.5 vs. 62.3  1.3

15 Improving the reliability of your measurements

16 T-test t test : used to compare one set of measurements with another to decide whether or not they are different.

17 Case A: t test for comparison of means : where P86

18 Case B significantly different comparing replicate measurements. 1904 Nobel Prize by Lord Rayleigh. for discovering Inert gas argon :

19 F calculated = (S 2 1 /S 2 2 )

20 t test for comparison of means : where P86

21 Examples case : comparing a measured result with a “known” value Sample: 3.19 wt% (known value) a new analytical method : 3.29, 3.22, 3.30, 3.23 wt% = 3.26 0 S = 0.04 1

22 Does answer agree with the known answer ? 95% confidence t calculate > t table  result is different from the known value.

23 Cholesterol content (g/L) SampleMethod AMethod BDifferent (d i ) 11.461.420.04 22.222.38-0.16 32.842.670.17 41.971.800.17 51.131.090.04 62.352.250.10 = 0.06 0 Examples case : Comparing individual differences

24 ∴ two techniques are not significant different at the 95% confidence level

25 4-5 Q test for bad data help decide whether to retain or discard a datum

26 Q calculate > Q t  discard  any datum from a faulty procedure.

27 4-5 Grubbs Test for an Outlier Then compute the Grubbs statistic G, defined as (4-6) If G calculated from Equation 4-9 is greater than G in Table 4-6, the questionable point should be discarded. Common sense: any datum based on a faulty procedure should be discard, no matter how well it fits the result of data. help decide whether to retain or discard a datum

28 TABLE 4.6  G calculate > Gt discard ---) any datum from a faulty procedure.

29 4-4 Finding the “Best” straight line calibration methods  prepare calibration curve. P93

30 4-4 Finding the “Best” straight line

31 4-7 Constructing a Calibration Curve 1) Blank standard soln Table 4-6 Spectrophotometer readings for protein analysis by the Lowry method Sample (μg) Absorbance of three independent samples Range Corrected absorbance ( after subtracting average blank ) 00.099 0.1000.001-0.000 3 0.000 7 50.1850.1870.1880.0030.085 7 0.087 7 0.088 7 100.2820.272 0.0100.182 7 0.172 7 150.3920.3450.3470.047---0.245 7 0.247 7 200.425 0.4300.0050.325 7 0.352 7 0.330 7 250.4830.4880.4960.0130.383 7 0.388 7 0.396 7 Standard soln blank 

32 4-7 Constructing a Calibration Curve 2) Finding the protein in an unknown


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