Chapter 7. Statistical Analysis Data Treatment and Evaluation Flashcards
occurs when we reject the hypothesis that two
quantities are the same, when they are statistically identical.
type I error
occurs when we accept that they are the same
when they are not statistically identical
type II error
most common applications of statistical data treatment :
- confidence interval
- the number of replicate measurements
- estimating the probability that two experimental means are different
- determining the precision of two sets of measurements differs
- comparing the means of more than two samples
- reject/retain a result
is the probability that the true mean lies within a certain
interval and is often expressed as a percentage.
confidence level
The probability that a result is outside the confidence interval
significance level
help determine whether a numerical difference is a result of a real difference (a systematic error) or a consequence of the random errors
inevitable in all measurements.
statistical test
assumes that the numerical
quantities being compared are the same.
null hypothesis
t approaches z as the number of degrees of freedom becomes large.
In each of these situations, the populations have differing values of a
common characteristic called a factor or sometimes a treatment.
In the case of determining calcium by a volumetric method, the factor of
interest is the
analyst
The different values of the factor of interest are called
levels
Analyst is considered a factor, while analyst 1, analyst 2, analyst 3, analyst 4,
and analyst 5 are levels of the factor.
The comparisons among the various populations are made by measuring a response for each item sampled
is the simplest method in which a
difference is calculated that is judged to be the smallest difference that is
significant.
least significant difference method
a result that is quite different from the others in the data set.
outlier
