1
Q
Define data handling
A
- Refers to the process of gathering, recording and presenting information in a way that is helpful to others - for instance, in graphs or charts
- AKA statistics
2
Q
Explain the set of skills included in data handling
A
- Collecting data using a planned methodology
- Recording data with precision and accuracy
- Analyzing data to draw conclusions
- Sharing data in a way which is useful to others
3
Q
Explain the overall info about data handling
A
- Analytical Science is the science of QUANTITATIVE measurement
– Data should be correctly reported
– Simple data manipulation must take place
– Requires keeping track of SIGNIFICANT FIGURES
– Uncertainty in a result must be identified
4
Q
Explain the evalution of experimental data
A
- Data reduction and analysis for sources and magnitude of uncertainty
- These are essential aspects of scientific measurements, yet are often given superficial treatment in undergraduate chemistry
5
Q
Define significant figures
A
Each of the digits of a number that are used to express it to the required degree of accuracy, starting from the first non-zero digit
6
Q
What are the 3 rules for significant figure determination
A
- Non-zero digits are always significant
- Any zeros between two significant digits are significant.
- A final zero or trailing zeros in the decimal portion ONLY are significant
7
Q
What are the general rules for significant figures
A
- Measurements and results should always be expressed in figures that are physically significant
- Only one uncertain figure is retained
- The uncertainty of a result could be “absolute” or “relative”
- Significant figures in mathematical operations (addition, subtraction, multiplication, division)
8
Q
Define Accuracy and how you evaluate it
A
- A measure of how close a measurement comes to the actual or true value of whatever is measured
- To evaluate the accuracy of a measurement, the measured value must be compared to the correct value
9
Q
Define Precision and how you evaluate it
A
- A measure of how close a series of measurements are to one another, irrespective of the actual value
- To evaluate the precision of a measurement, you must compare the values of two or more repeated measurements
10
Q
Explain the accepted and experimental value
A
Accepted value, which is the correct value for the measurement based on reliable references, and the experimental value, the value measured in the lab
11
Q
What is “ error “
A
The difference between the experimental value and the accepted value
12
Q
What are the 3 types of error
A
- Systematic error
- Random error
- Gross error
13
Q
Explain systematic error
A
- Problem with a method, all the errors are of the same size, magnitude and direction (Determinate errors)
(Sources: Instrumental, personal, method error)
14
Q
Explain random error
A
- Is based on limits and precision of a measurement (Indeterminate errors)
- Typically has high precision, variable accuracy. Difficult to control for. Best described by Gaussian curve
15
Q
Explain gross error
A
“BIG MISTAKES”. Lead to outliers - Best to just repeat the work
16
Q
How can error be propagated
A
- Through a series of calculations (addition, subtraction, multiplication, division, significant figures)
- As a relative or an absolute error
17
Q
What are the 3 types of determinate errors
A
- Instrumental errors
- Operative errors
- Errors of Method
18
Q
Explain Instrumental errors
A
- Most common error
– Normally has a random distribution
19
Q
Explain Operative errors
A
- Personal errors
– Errors in calculation
20
Q
Explain errors of method
A
- Most serious error
- Could be corrected (by comparison with a blank)
21
Q
What are types of intermediate errors
A
Accidental or random errors
22
Q
Explain Accidental or random errors
A
- Should follow a “normal distribution” – Almost impossible to eliminate entirely–
e.g. Human error (poor eyesight, color blindness)
e.g. Fluctuations in temperature
e.g. Small differences in sample volumes used
23
Q
Define absolute error
A
A measure of how far ‘off’ a measurement is from a true value or an indication of the uncertainty in a measurement
24
Q
Define relative error
A
- A measure of the uncertainty of measurement compared to the size of the measurement
- AKA relative uncertainty or approximation error
RE = Absolute error / Actual Value
25
Define median
- In a group of measurements arranged from lowest to highest, the middle value if the
number of measurements is odd
- If the number of measurements is even,
the median is the average of the two middle values
26
Define the mean
Is the average of the numbers. It is easy to calculate: add up all the numbers, then divide by how many numbers there are
27
Explain standrad deviation trends
Higher standard deviation indicates higher spread, less consistency, and less clustering
28
Explain normal distribution (Z)
Is a probability function that describes how the values of a variable are distributed.
29
Explain Normal Distribution Curve (Gauss curve)
It is a symmetric distribution where most of the observations cluster around the central peak and the probabilities for values further away from the mean taper off equally in both directions
30
Explain Normal Distribution Curve (Gauss curve) in more detail
1. A mathematical expression of the indeterminate error
2. Has the following characteristics :
– a maximum at zero determinate error
– a symmetric form around the maximum
– an exponential decrease in frequency as the error increases
31
What is a histogram
- A graphical representation of the distribution of numerical data.
- It is an estimate of the probability distribution of a continuous variable (quantitative variable)
32
Speak about the characteristcis of a normal distribution curve
- Are symmetrical with a single central peak at the mean (average) of the data.
- The shape of the curve is described as bell shaped with the graph falling off evenly on either side of the mean
- Fifty percent of the distribution lies to the left of the mean and fifty percent lies to the right of the mean
- The spread of a normal distribution is controlled by the standard deviation, . The smaller the standard deviation the more concentrated the data.
- The mean and the median are the same in a normal distribution
33
Explain confidence limits
- The statistical probability that the true (correct) value falls within a certain range
- This range is the “confidence interval
34
Explain a confidence interval
- A type of estimate computed from the
statistics of the observed data. This proposes a range of plausible values for an unknown parameter
- The interval has an associated confidence level that the true parameter is in the proposed range
35
Explain F tests
- Any statistical test in which the test statistic has an F-distribution under the null hypothesis
- It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fit the population from which the data were sampled
36
How are f-tests defined
Is defined in terms of the variances of two methods
37
Explain control charts fully
- A sequential plot of some quality characteristic that is important in quality assurance
– Chart shows the statistical limits of
variation that are permissible (e.i: Control chart for a modern analytical balance)
38
Explain quality assurance
Provides the producer/user of the service the assurance that it meets the needs of the user
39
Explain quality control and quality assessment
- Control of the quality of a product or service
- Provide assurance that the overall control job is done effectively
40
What is the correlation coefficient
A numerical measure of some type of correlation, meaning a statistical relationship between two variables
41
Define / Explain the least squares method
- A statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve
- Least squares regression is used to predict the behavior of dependent variables
42
Define T-tests
The t-test is any statistical hypothesis test in which the test statistic follows a Student's t-distribution under the null hypothesis
43
Explain T-tests
- Comparison of two sets of data made by two different methods
- A statistical “t value” is calculated and compared with a tabulated value
- For results to be acceptable t (calculated) should not exceed t (tabulated).
- The “t test” can be applied to multiple samples
44
Explain Q-tests
- The Q test is a simple, widely used statistical test for deciding whether a suspected results should be retained or rejected
- Dependent on the number of data points
CHEM 212
flashcards
Decks in class (14)
# Cards
-
Statistical Analysis - T344
-
Gravimetric methods of Analysis - T346
-
Volumetric Analysis - T336
-
Titrations - T332
-
Acid Base indicators - T37
-
Electrochemistry30
-
Gases - T462
-
Thermodynamics - T423
-
2nd Law of Thermodynamics - T423
-
Entropy Change ( 2nd Law ) - T425
-
Electrolytic conductance28
-
Precipitation Titration - T329
-
Complex Titrations - T318
-
Miscellaneous P112
