Download Laboratory Instrumentation and Test Interpretation

Laboratory Instrumentation and Test Interpretation
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Statistical Interpretation
I.
Central Tendency and Dispersion: Whenever you are discussing the distribution
of a data set, you should consider it’s central tendency and dispersion.
A. Central Tendency: The two most common measures of central tendency are:
1. Mean:
2. Median:
B. Dispersion: There are many ways to quantify the dispersion of a distribution. The
most common method that is found in scientific articles is the:
1. Standard deviation:
II.
The Concept of Statistical Significance:
A. What is it?:
1. Statistical significance relies on the laws of probability to determine whether
an observed difference between means is too large or too small to occur by
chance.
a. How is probability denoted?
b. What is the p-value?
c. How is the p-value interpreted?
d. The higher the alpha value, the easier it is to achieve significance,
however the scientist is less sure of the results.
i.
What would be an appropriate time to use a p-value of 0.10?
ii.
III.
What would be an appropriate time to use a p-value of 0.01?
Tests of Significance:
A. What do they do?:
B. Common Tests of Significance:
1. T-test: Used to determine if there is a significant difference between two
means. Examples:
a. Test means between two different groups:
i.
Example:
b. Test means pre-post with the same group of people:
i.
Example:
c. Test mean of one group against a known standard or expected standard:
i.
Example:
2. ANOVA: Identical to the t-test except you test three or more means.
a. There are many different forms of ANOVA.
C. How to Interpret the Results of a Test of Significance:
1. P-value:
a. Check the methods section to determine alpha.
b. Check the results to see if the p-values are less than alpha.
c. If less than alpha, the groups are significantly different.
2. What factors will affect the p-value?
3. Practical vs. statistical significance:
IV.
Degree of Relationship:
A. Two Types of Relationships.
1. Direct/positive vs. indirect/negative relationships:
a. Direct/positive relationship:
i.
Examples:
b. Indirect/negative relationship:
i.
Examples:
2. Note: Relationships do not imply cause and effect.
B. The Correlation Coeffecient: The statistic used to determine the strength of
relationship between any two variables.
1. How is a correlation denoted?
2. What do the values mean?
3. Most articles will also include a p-value to determine whether the relationship
is statistically significant.
a. Examples:
i.
Relationship (r = 0.52, p = 0.03)  moderate but significant
relationship.
Relationship (r = 0.79, p = 0.18)  strong by insignificant
relationship.
b. Sample size plays a large role in regards to significance.
ii.
C. Interpretation of the Correlation Coefficient:
1. In general, the following rules can be applied:
a. 0.00 – 0.29 = low relationship
b. 0.30 – 0.59 = moderate relationship
c. 0.60 – 0.84 = moderately high relationship
d. 0.85 – 1.00 = high relationship
D. Other Applications of the Correlations Coefficient:
1. To determine validity of instruments.
2. To determine inter-tester validity of instruments.
3. To determine test-retest validity of instruments.