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Descriptive and Inferential Statistics
Descripción
Degree (Methodology and Statistics) Psychology Mapa Mental sobre Descriptive and Inferential Statistics, creado por natalieclark29 el 24/11/2013.
Sin etiquetas
methodology and statistics
psychology
second year university
psychology
methodology and statistics
degree
Mapa Mental por
natalieclark29
, actualizado hace más de 1 año
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Creado por
natalieclark29
hace más de 10 años
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Resumen del Recurso
Descriptive and Inferential Statistics
Descriptive Statistics
Measures of Central Tendency
A single score that represents the data
For example
Mean
Median
Mode
Measures of Dispersion
A measure of variability within the data
For example
Standard Deviation
Using means and SDs we can...
Z-scores
Compare a range of measurements
Express how many SD units a point in the normal curve is from the mean
A z-score is the number of SD units a score is from the mean
Summarise data visually
Populations
Every single possible observation
We know how these tend to be distributed
Samples
Make inference about the population from the sample
Summarise from sample data what population mean is
Central Limit Theorem
Representative samples
sampling distribution approaches normal distribution
Mean of all sample means equals population mean
Sample size increases, SD of sampling distribution decreases
Sample size increase = more certainty of population mean
Standard Error
Confidence that sample mean represents population mean
Inferential Statistics
Allow us to make inference or generalisation
Is this group different from the population?
Are these two groups different from each other?
Does an experimental manipulation have an effect?
Basic Principles
Test statistic
How different your sample is from another mean
Critical value
Alpha level
p<0.05
Test statistic < critical value at alpha level = significant results
Errors
Type I
Too loose alpha level
Type II
Too strict alpha level
Student's t-distribution
Gossett
Worked for Guinness in Dublin
Assessed grain quality
Used small samples to make assumptions about general population
Published under a pseudonym
Realised that...
As sample n gets larger, sampling distribution looks more normal
Small samples, pointier around mean and fatter at edges
Shape differs at all degrees of freedom in a sample
t-tests
Uses
Single sample is drawn from a population where mean is known
Between samples t-test
One Sample Wilcoxon Signed Ranks T-test
Two sets of measurements are drawn from same population
In different groups
Independent samples t-test
Mann-Whitney U
Before and after intervention
Paired samples t-test
Wilcoxon Signed Rank T-test
Quantifying the differences in the data relative to the variation that exists in the data
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