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58 Cards in this Set
- Front
- Back
- 3rd side (hint)
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p- values,
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the probability that whatever pattern or difference found in data came across by chance
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by chance
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Null hypothesis,
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means there is no difference/pattern in data
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Cut off rate for p value,
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0.05
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Discrete/ categorical,
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nominal and ordinal
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Continuous/ numeric,
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interval and ratio |
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Interval,
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no absolute zero , can’t multiply or divide values e.g. temperature
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Ratio,
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absolute zero, arithmetically meaningful, add subtract multiply divide
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Interquartile range,
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difference between 1st and 3rd quartile
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Variance,
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the average distance from the mean
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Standard deviation,
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square root of variance
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Central limit theorem,
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the sampling distribution of the sample mean approximates the normal distribution
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Histograms,
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continuous data, shows frequency
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Bar Charts,
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discrete data, shows frequency |
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Box whisker plot,
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shows median, quartiles, min, max and outliers |
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Scatter plot,
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continuous data, relationship between two variables |
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Error bar chart,
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shows mean and 95% confidence intervals
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Pie charts,
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discrete data, shows proportions
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Unimodal,
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one peak in the distribution |
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Normal distribution,
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mean, mode and median are the same, a symmetrical distribution
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Skewness,
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is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean
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Kurtosis,
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describes how pointy the distribution is
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Leptokurtic,
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pointy distribution
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Mesokurtic,
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similar to a normally distributed data set |
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Platykurtic,
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almost flat |
almost flat
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Rules for normal distribution,
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If skewness and kurtosis aren’t 2x standard error score means you have a normal distribution |
If skewness and kurtosis aren’t 2x standard error score means you have a normal distribution
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Why is normal distribution useful?,
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We know the probability of a score falling within certain number
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Z scores,
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converting your real scores to standardized scores, which follow the normal distribution
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t-test,
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examines whether two sample means are different
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one smaple t-test,
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tests whether mean of a sample is different to a specific number
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within groups t-test,
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compares mean of same sample in two different conditions
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between groups t-test,
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compares mean of two different samples in different conditions
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p = .001,
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means we’ve got a significant difference
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Wilcoxon’s signed ranks test,
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nonparametric equivalent to the paired samples t-test converts data to ranks
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Mann-whitney’s U,
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nonparametric equivalent to independent samples t-test
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Levene’s test,
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an inferential statistic used to assess the equality of variances for a variable of two or more groups
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Correlation,
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the similarity of 2 variables within a group
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Positive relationship,
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as one variable increases, the other increases
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Negative relationship,
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as one variable increases, the other decreases
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Third variable,
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there’s a third variable causing the relationship
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Variance,
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is a measure of dispersion |
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Covariance,
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how much the two variables vary together
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Pearson’s r,
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a measure of the linear dependence between two variables
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Pearson’s r scores,
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1- a perfect positive relationship, 0 – no relationship, -1 – a perfect negative relationship
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Pearson’s r assumes…,
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a linear relationship
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95% Confidence intervals,
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means we can be 95% confident the population will fall into upper and lower bounds
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R2 ,
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tells how much shared variance between your variables
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R2 = 1,
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(100% shared variance between two variables)
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R2 = .25,
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25% shared variance between two variables
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Spearman’s P,
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the non parametric equivalent to Pearson’s r, converts scores to ranks than uses Pearsons equation
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Partial correlation,
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if there are say three variables we can partial out one variable
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Correlation coefficients,
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used to measure how variables covary with each other |
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Univariate graph,
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a graph with only one variable e.g. histogram
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Statistical tests,
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t-test and correlation test
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Bivariate data,
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two variables represented at the same time e.g. scatter plot
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