Descriptive and Inferential Statistics

Mind Map by , created almost 6 years ago

Degree Psychology (Methodology and Statistics) Mind Map on Descriptive and Inferential Statistics, created by natalieclark29 on 11/24/2013.

Created by natalieclark29 almost 6 years ago
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Descriptive and Inferential Statistics
1 Descriptive Statistics
1.1 Measures of Central Tendency
1.1.1 A single score that represents the data
1.1.2 For example Mean Median Mode
1.2 Measures of Dispersion
1.2.1 A measure of variability within the data
1.2.2 For example Standard Deviation
1.3 Using means and SDs we can...
1.3.1 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
1.3.2 Summarise data visually
1.4 Populations
1.4.1 Every single possible observation
1.4.2 We know how these tend to be distributed
1.5 Samples
1.5.1 Make inference about the population from the sample
1.5.2 Summarise from sample data what population mean is
1.5.3 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
1.5.4 Sample size increase = more certainty of population mean
1.5.5 Standard Error Confidence that sample mean represents population mean
2 Inferential Statistics
2.1 Allow us to make inference or generalisation
2.1.1 Is this group different from the population?
2.1.2 Are these two groups different from each other?
2.1.3 Does an experimental manipulation have an effect?
2.2 Basic Principles
2.2.1 Test statistic How different your sample is from another mean
2.2.2 Critical value
2.2.3 Alpha level p<0.05
2.2.4 Test statistic < critical value at alpha level = significant results
2.3 Errors
2.3.1 Type I Too loose alpha level
2.3.2 Type II Too strict alpha level
2.4 Student's t-distribution
2.4.1 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
2.5 t-tests
2.5.1 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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