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Stem and Leaf Plot
 Shows the frequency of happenings in stats
 x
 xxx
 xx
 xxxxx
 xx
 x
 xxxxxxxx

Histogram
Frequency chart shown as bars


Bell Curve
A chart showing the standard deviation from the normed center of the plotting

Nominal Scale
 Least Precise
 Puts Variables into Categories
 Like race, socioeconomic

Chi Squared
Designed to measure the significance between 2 variables

Ordinal
 2nd Least Precise measurement
 Categorizes by magnitude highest to lowest
 Class rank
 No real detail about why the grades are the way they are, just that they are

Interval
 The Most Used for of Measurement in Education
 Catergories by magnitude and understanding the why of the intervals in between
 No Absoulte Zero...there will always be something
 Uses Pearson r for correlation
 Be wary of using this to make predictions...not always cause & effect

Ratio
 Most Precise
 Categories of data showing magnitude
 Intervals are the same size and there is true zero
 Data can be manipulated

Descriptive Statistics
 Looking at the numbers and doing things like:
 Averages
 Median
 Mode
Correlation is not Causation
 Cummulative Percentage Chart
 Spearman is all about the rank order of things

Central Tendency
Single Value that is considered the most typical...a norm at which to determin standard deviation

Validity
Using the right tool to measure correctly
Content Validity the items on the instrument refelct the content
Construct Validity the extent to which a higher order construct is represented in a study (Help Seeking, stress, dyslexia...etc.)

Reliability
Having reliable scores
Having temporal stability stability over time
Angoff Method standard setting

Internal Validity
The ability to infer that a causal relationship exists between 2 variables

External Validity
The extent to which the study can be generalized and applied across populations

Errors of Measurement
Standard Deviations from the norm
Always going to have errors in samples, mathematical
The small the standard deviation the more accurate the measure

Standard Error of Measurement
The standard deviation of a sample population

Null Hypothesis
A statement about a population parameter some condition concerning the pop. parameter is true.
prediction of no difference in a study when a new treatment is given

Rejecting the Null Hypothesis
When there is a relationship between populations due to the given treatment you reject the null hypothesis

Accepting the Null Hypothesis
Means you are admitting that there is no relationship between populations given the treatment

Inferential Statistics
Inferring information from a sample to a larger population

Types 0f Samples
 Random Sample
 Sampling Interval
 Stratified Sample
 Cluster Sample
 Convenience Sampling
 Quota Sampling
 Purposive Sampling (Judgemental)
 Snowball Sampling

Type One Error
To say it had an effect, but it really didn't have one

Type 2 Error
To say the treatment did not have an effect when it really did

Power
The larger the sample size the more power a test has...it is more sensitive. The Bigger the sample size the better...less likely to have a Null hypothesis

Effect Size Indicator
A statistical measure to show strength of a relationship

Practical Significance
A conclusion made when a relationship is strong enough to be of practical importance

t Test
Used to compare 2 group means
Used to determine if the difference between sample populations was created merely by chance errors or really because of the treatment

ANOVA
ANOVA is used to compare one or more group means
Can compare more than 2 groups
Gives a p value

Scientific Method
 Question
 Research
 Hypothesis
 Test
 Analyze
 Test Again
 Report

Alternate HypothesisExample
Male and Female population's means on SAT tests are different

Null Hypothesis Example
Male and Female population means on not different on the SAT

Pearson Product Moment Correlation
Shows a correlation between 2 ideas
Higher tuition correlates to lower enrollment
Tested with the Pearson r

