Test 1 Flashcards
(51 cards)
Null Hypothesis
no difference between groups
Alternative Hypothesis
Why you might see the opposite or different result in your data
Scale Variable
ratio-interval scale with true zero point. Numbers can be compared as multiples of each other
example: years
interval–meaningful numbers with no true zero point. equally sized intervals
example: temperature
Nominal Variable
- nominal categories are differentiated
- male and female
Ordinal Variable
- information is ranked, placed in order of its position on a scale
- sometimes originally scale data and changed to ordinal like small medium large
- not quantitative
Continuous Data
- range depending on ability to measure
- ratio, ordinal and interval may be continuous or discrete
example: height of plants
Discrete Data
- integers
- nominal falls here
example: number of children, leaves
Models
- the explanation of an observed pattern is a model–thought or word or math
- series of statements that explain why observations have occurred
- verbal are non-math, but can be quantified with math
- empirical models are math equations to predict
- theoretical models study the processes themselves
Sample
- collection of observations
- the number is called the sample size
- measured characteristics is called statistics
- characteristics are called parameters
Statistical Inference
-inferring about a whole population from a sample
Simple Random sampling
-basic method of collecting
have to build this in
-have a machine pick a number for you to sample
Stratified Sample
-where you know there’s heterogeneity
-divide into homogeneous subgroup, no overlap of cluster groups
HETERO
Systematic sampling
-establish groups, take the nth group every time. HOMO
Clustered groups
- mix of stratified and systematic
- establish clusters and know they are HOMO within so can take systematic samples
What is random sampling
-underlying assumption of essentially are inferential stats
-all possible measures in population must have equal chance of being chosen
BIASED
-when certain measures are more likely to appear in sample than others
-can be ignored if known to have no effect
Significant Figures
-number you use tells you your range of error
rule 1: keep as many as possible to reduce rounding error
rule 2: for a mean, report one more place than data (lat one is uncertain) example: you say 2 cm. means one cm of error. so 1.5 to 2.459
DON’T overstate your accuracy. Naughty.
Frequency Distribution
- number of observations per category
- can visually graph and see that there is a normalish distribution and move forward
- understand underlying shape and identify outliers
Measure of central tendency
- stat that describes the concentration of middle of sample
- mean and median usually
Arithmetic mean
- mean or average for a population
- informs us of a central point of sample, hopefully population matches
Weighted arithmetic mean
- allows you to weight for frequency of sample values
example: average grades per class period , but may be most students in middle section so weight that one heavier than the others.
Probability
- likelihood of a given event expressed as either relative frequency or knowledge of given system
- if you repeat infinite number of times, what percentage will turn out this way?
Relative Frequency
- freq of all events/# of all events
- ranges 0 to 1 (0 to 100%)
- may be done with or without replacement
Permutation
arrangement in specific sequence
- linear n! or circular n!/(2n)
- taken X at a time n!/(n-X)!
- number combos n!/X!(n-X)!
Mutually exclusive events
- don’t overlap. ex. You can’t be on time and late at once
- ADDITION
