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ENVS 1030 Study Guide - Final Guide: Type I And Type Ii Errors, Null Hypothesis, Dependent And Independent Variables


Department
Environmental Sciences
Course Code
ENVS 1030
Professor
Shelley Hunt
Study Guide
Final

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ENVS FINAL 12/17/2015
Scientic method
observation
hypothesis (general)
prediction (specic)
test (experimental observation
update probability
Observational vs manipulative studies
correlation vs causation
ocorrelation does not imply causation
relation to the problem does not imply it is the problem
directionality problem: don’t know which variable
is actually the cost
3rd variable problem: the problem might be caused
by a third factor/variable
Sample: is a subset of the entire group of interest
Random sample: is made by choosing in an unbiased way
by numbering the individuals and drawing numbers from a hat
Randomize
we want each unit to have an equal chance of being chosen for
oinclusion in the sample being measure (observational study)
oor, inclusion in a given treatment group (manipulative study)
oavoid BIAS

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Type I error
the null hypothesis is true but you reject the null hypothesis (alpha)
Type II error
the null hypothesis is false but you fail to reject the null hypothesis
(beta)
Relationship between TYPE I AND II errors
decreasing the probability of type I error (alpha) increases the
probability of a type II error (beta)
for any xed alpha, an increase in the sample size n will cause a
decrease in beta
for any xed sample size n, a decrease in alpha will cause an
increase in beta
for any xed sample size n, an increase in alpha will cause a
decrease in beta
to decrease both alpha and beta, increase the sample size
Controls
negative: leave it exactly as it is (ex, give a group drugs while not to
others)
positive: placebo (ex, give some drugs others placebo)
historic: using old data, other control ideas
concurrent: make your own control
Blind procedures
a person measuring the dependent variable (s) is unaware of
treatment
addresses potential bias
Blocking
controlling for variables that might introduce more variation in your
response variable (ex, age of dog)
Split plot design

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two factors
grouping by one factor
random allocation for second factor
convenience
Tradeo9 sampling
tradeo9 between sample size and information
Interactions (covariates)
the relationship between mating success and brightness of male
sh exterior
ointeractions or no interactions
Logical fallacy= ;awed reasoning
a claim or argument attempting to sway you without good evidence
oHasty generalization
a study shows that BPA is present in the urine of babies
who drink for plastic bottles “babies are being poisoned
by their bottles”
oRed Herring
BPA may be harmful, but plastic companies employ a
lot of people”
oAd hominem attack
BPA should be banned because the chemical industry is
untrustworthy and greedy)
oappeal to authority
“The national toxicology program is concerned about
the health e9ects of BPA, so it must be a health hazard”
oFalse dichotomy
BPA must be completely avoided” “BPA is perfectly safe
for everyone” “Since BPA is harmful in some cases, it
must be avoided by everyone”
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