Lab Manual Appendix B and C Flashcards
measurement error
reflects the discrepancy between our repeated measurements and the true value of the object being measured
precision
the repeatability of the measurement
accuracy
the tendency for the measured value to be close to, or approximate, the true accepted value
sample size is abbreviated by
n
population size is abbreviated by
N
title for a graph
placed at bottom of a graph
interpolation
a line of best fit through the data that allows you to predict values of the dependent variable for which you do not have actual data points
extrapolation
the process of extending the line beyond the existing data, maintaining the trend shown by interpolation, and provides a basis for prediction
bar graph
used for discrete quantitative variables which are similar but not necessarily related
histogram
used exclusively for showing the distribution of data that are continuous
statistics of location
describe the position of a sample along a given dimension
- most common statistics of location are the mean, median and mode; these are called measures of central tendency
statistics of dispersion/variability
describe the distribution of observations.
- simplest is the range, and most common are the variance and standard deviation and standard error
variance
measure of how much scatter there is around the mean
(n-1)
degree of freedom. adjustment to have an unbiased estimate of the variance from a sample
standard deviation, s
average size of the deviation from the mean
standard error
measure of how reliable the sample mean is as an approximation to the population mean
test of significance
evaluates the probability of rejecting the null hypothesis when it is actually true
student’s t-test
statistical test that compares the means of two samples and assesses whether or not they differ enough to represent samples from different populations
significance level
the probability of mistakenly rejecting the null hypothesis when it is true
draw graph for t test acceptance/rejection region
chi square test / goodness of fit /contingency
used to test a hypothesis in an experiment in which the data are frequency data rather than continuous date
- tests difference between observed and expected frequencies and is used for data that can be arranged into classes such as habitat, genotype, percentage
assumptions of a chi square test
- data must consist of samples taken at random from a large population
- for each sample taken there is a restricted number of outcomes that can be divided into distinct categories
- probabilities for each of the outcomes are independent from each other and do not change from one sampled individual to another
draw graph for acceptance/rejection region for chi squared distribution
when would you use a 2 x 2 contingency table?
when an analysis is concerned with two variables
what did Hardy and Weinberg realise?
that the genotype frequencies of a population could be predicted from the frequency of the alleles, represented as p and q, using the binomial equation:
p^2 + 2pq + q^2
the above equation describes a single locus with two alleles
what is necessarily true for the HW equation?
frequencies of the two alleles (p + q) must equal 1 and the frequencies of the genotypes (p^2 + 2pq + q^2) must also equal 1.
two functions of HW equation
- calculate genotype frequencies of a pair of alleles at a gene locus
- predict genotype frequencies of the next generation as long as there have been no changes in the gene pool
when there have been no changes in the gene pool, genetic equilibrium will only occur when:
- population is large enough to be unaffected by random gene changes (i.e. genetic drift)
- there is no gene flow (immigration or emigration)
- no mutations occur or there is mutational equilibrium
- reproduction is random (independent of genotype)
- natural selection is not acting on a particular phenotype
why is the concept of genetic equilibrium an important concept if in natural populations all of the conditions are almost never met?
significant deviations from the expected HW values provide evidence that some evolutionary force is in action. We can think of the expected HW values as a null hypothesis (no evolutionary force is acting on the population)
