Flashcards
Stats flashcards
Cards in this deck (67)
- 1(dependent variables) define: nominal, ordinal, interval, ratio.Nominal- categorical (type of beer).
Ordinal- relative position (rank of beer).
Interval- difference between 2 values meaningful(mean temp of beer).
Ratio- similar interval w/ meaningful zero point(beers per night). - 2Define within-subjects and between-subjects designs.Within subjects(repeated measures), group experience both conditions.
Between-subjects, 2 groups experience one condition. - 33 measures of central tendencyMean. Mode and Mediun.
- 4Define standard deviation and variance? (And why?)*variance = sum of squares / number of items
*standard deviation brings variance back to original measures (not squared :P)
*Variance = (Std Deviation)^2
*the deviation of individual samples can be positive or negative, by simply adding them together they would cancel each-other out, by squaring the std deviation it truly represents the total variance. - 5Normal DistributionA function the represents the distribution of many random variables as a symmetrical bell-shaped graph.
- 6What is a Gaussian equation?An equation to find the height of the distribution at any point:
- 7Define a positive-skew, negative-skew (and un-skewed)
- 8What are Z-scores? (and how are they calculated?)They tell us how many standard deviations are above/below the mean value.
*calculated by taking the observation, subtracting it from the mean of all observations, and dividing the result by the standard deviation of all observations.
*by converting a distribution of observations into Z-scores a new distribution is created that has a mean of 0, and a standard deviation of 1. - 9How to determine sampling error.if you take a random sample from population of values to estimate parameters of original population. BUT the mean of each sample will differ from the true mean of the population.
- 10Standard Error of the Mean (SEM) - also called the 'confidence interval'essentially a measure of how confident we are that we know the true population mean, this is dependent on: variability of original data, number data used to create sample mean.
-standard deviation squared (negative results become positive)
-tells you the RANGE of values the mean might really take. - 11standard deviation vs SEMstdev=the amount that scores vary.
SEM=measure of confidence we know true population mean. - 12An easy example to explain degrees of freedom?if we keep alternating males + females around a table then (N-2) can choose their own seat.
- 13Define 'co-variance'How much 2 random variables change TOGETHER. If the 2 variables show very similar patterns the co-variance value will be positive, if they are very different the value is negative .
- 14Explain Spearman's rsA (Non-parametric) bivariate correlation coefficient (uses ORDINAL data) that relates to Pearsons Rho, first work out the ranking of the data then perform pearson's Rho(r^2) on the rank scores.
*ranking the data reduces the impact of outliers - 15Partial Correlations?sometimes a correlation between 2 variables might only be explained by another variable e.g. does excessive drinking affect the brain during exams?
-correlate how many times one gets drunk(A) with exam score (B)
-what about the fact that the drinking stopped one from being at home studying? (C) - 16Correlation terminology: -Partialled out? -Zero order correlation -first-order corr -second-order corr-Variable C is referred to as being 'partialled out'/held constant
-A simple correlation is also called a zero-order correlation.
-first order has one variable held constant
-second order has two variables held constant - 17What is 'power'?- the ability of a test to detect an effect, it can be used to determine how big a sample is required to detect a significant effect.
P= the probability of finding an effect given that one truly exists. (Ho is false)
denoted by 1-'Beta' - 18What does the Bonferroni correction do?divides the alpha value by the number of statistical comparisons made to see how many tests will give false results.
- 19What to do if there are low assumptions for data in a chi-square test?'collapse categories' and include smaller categories within other categories- or discard a small category and run the test on the remaining data.
- 20What is a 'binomial test '?used for data that can only have 2 values, compares whether frequency of occurrence for 2 values has some expected ratio e.g. whether a coin is giving heads suspiciously often or within the range expected by chance.
- 21What does an ANOVA do?*compares multiple groups (as using multiple t-tests would require an abnormally low alpha)
*compares the amount of variance explained by the experiment with variance that is unexplained. - 22For an ANOVA what is 'F'?F is the explained variance divided by the unexplained variance
- 23A repeated measures ANOVA?parametric test that establishes the differences between multiple groups in a repeated measures design
- 24What is regression analysis?a technique to fit a model (mathematical equation) to data, can be a very simple or complex (few or many parameters). Simplest non-trivial model is the linear model (y=a+bx) (equation of a line).
**Once the equation has been fitted to the data the slope of the relationship between 2 variables becomes clear and it is much easier to predict y given x etc. - 25regression analysis versus correlations?-correlations show that there is a relationship between the variables and how much noise contributes
-regression analysis tells us what the relationship is but does NOT tell us whether it is real. - 26Define Non-Parametric testswhere the data is not required to fit a normal distribution- this (usually) uses ordinal data so does not rely on numbers, but rather a ranking or order of sorts.
-parametric= preferred because more 'power' - 27What do resampling techniques do?measure sampling error by repeating the sampling process a large number of times. This can determine the likely error introduced by sampling by looking at the variability in the resampling.
- 28Between-subject randomisation tests (aka permutation tests)want to determine the likelihood of getting differences this extreme if the data all came from a single population. Simulate running the experiment many times, with data all coming from 1 population to check what range is normal. Keep measured values but SHUFFLE them (randomly assign them to 2 groups)- count how often the difference between the new means is bigger than between the measured means. Assume they are real and sensible values (do not assume anything about distribution).
- 29Within-subjects randomisation testin each group resample the values and shuffle for each subject rather than across the whole data set (randomise the sign of the difference for each pair)
- 30Bootstrap resamplescan be used to calculate: confidence interval of a mean or SEM etc. Can also determine whether some test values are inside or outside the 95% confidence interval (is a non-parametric method so makes very few assumptions of the data).
- 31A one-sample test (resampling stats)we can simply count how often a mean occurs within the bootstraps, order the data find how many values are the mean.
- 32Jack knife method (resampling stats)similar to bootstrap but rather than 'randomly sampling with replacement', resampling is done by 'selecting all data but one'.
- 33Monte-carlo method (resampling stats)create data based on model stimulations & compare these to real data..
- 34Perentile bootstrap confidence intervalorder bootstrap samples and trim the top and bottom 2.5% to create 95% confidence- see where the data lies.
- 35Describe error-bar plotsindicate the variability or confidence interval for a data point. Should be added whenever you display a mean of several measurements. Std-dev, SEM or CIS can all be used to make the error bars.
- 36Describe a Kruskal-Wallis testNon-parametric test that establishes the differences between multiple groups with one independent variable in an independent measures design.
- 37Describe Wilcoxen signed-rank testa non-parametric test that establishes the differences between 2 conditions that are experienced by 1 group.
- 38Describe a Friedman's ANOVANon-parametric test that establishes the differences between multiple groups in a repeated measures design.
- 39What does a significant test statistic tell you?that there is an effect in the population of sufficient magnitude to be scientifically interesting.
- 40Define Platykurtic and Leptokurtic distributions-Platykurtic distributions are shorter and flatter
-Leptokurtic distributions are taller and 'peakier' - 41Type 1 error definition and relation between power & alphafalsely accepting the exp hypothesis (false alarm)
probability = alpha
(probability that this occurred by chance is 0.05, probability of data must be less than alpha...) - 42In SPSS what is the correct way to record non-numerical values?to define the variable as "string".
- 43What does a Chi-Square test do?compares the frequencies observed in certain categories to the frequencies you might expect to get in those categories by chance using a contingency table.
- 44Describe a Chi-square good-ness of fit testuses one categorical variable from a single population
- 45Describe a Pearson's Chi-square testEXAMPLE: - you are asking adults which fizzy drink they prefer: Pepsi or Sprite, and comparing the answers given by each gender.
- 46Describe an independent samples t-testparametric test that establishes whether two means collected from independent samples differ significantly.
- 47Describe a related-sample t-testparametric test that establishes the differences between 2 conditions that are experienced by 1 group.
- 48Describe a One-way ANOVA:Parametric test that establishes the differences between multiple groups with one independent variable in an independent measures design.
- 49Describe a factorial ANOVAa test that establishes the differences between multiple groups using multiple independent variables.
- 50What are post-hoc tests?set of comparisons between group means that were not thought of before data were collected. Typically involve comparing the means of all combinations of pairs of groups.
*less power than planned contrasts & are usually for exploratory work for which no firm hypotheses were available on which to base planned contrasts. - 51What is a residual?The difference between the observed value of the dependent variable and the predicted value
- 52Describe heteroscedasticity and homoscedasticity- hetero= circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it.
- homo= circumstance in which the variability of a variable is equal across the range of values of a second variable that predicts it. - 53What does a test of homoscedasticity of error terms determine?whether a regression model's ability to predict a DV is consistent across all values of the DV.
- 54what does an assumption of homogeneity and heterogeneity of variance mean?*same as tests of homo/hetero-scedasticity but for an ANOVA.
- 55Describe Levene's test:a test of homogeneity for multiple groups (with independent ANOVA). A significant result implies that the variances are DIFFERENT.
*when sample sizes are large, small differences in group variances can produce a significant Levene’s test. - 56Describe the assumption of sphericitythe assumption of equal variance between multiple variables and multiple groups (ANOVA)
- 57What is standardisation?the process of converting a variable into a standard unit of measurement. The unit of measurement typically used is 'standard deviation unit' (z-scores).
*allows us to compare data when different units of measurement have been used. - 58How much variance is explained by a correlation? (e.g. 0.9)the percentage of variance that is explained is equal to the square of the correlation coefficient
(e.g. 0.81) - 59Why mustn't the expected values of a chi-square fall below 5(ish)?if the individual values are low the category totals are likely to overlap.
*need a more specific/sensitive test. - 60Supposedly how should you compensate for heterogeneity of variance in an independent samples t-test?by correcting the degrees of freedom.
- 61in a factorial design with 2 factors, if the effect of one factor appears to depend on the levels of the second factor this is called:an interaction effect.
- 62an experiment was conducted in which participants were given lists to learn in either quiet or noisy environments. Later, recall was either in the same or a different context of learning. What design is this experiment?Two factorial (2 IV factors) fully independent measures (both are between-subjects).
- 63In linear regression the variable that is being predicted (y) is called:*dependent variable
*criterion variable - 64How to calculate the Total Error/Sum of Squares*Square the deviations of the mean (result-mean) and add them together, need to square them so positive + negative don't just cancel each other out!
*divide by number of items=Variance! - 65How to calculate the expected frequency off of a contingency table: *for example frequency of men that prefer sport (both genders and several favourite tv shows)(number of men/total people) X (number of people that like sport/Total) XX the Total
- 66Define: *Mean Square *Distribution-free-tests *B/b (not Beta)*a sum of squares divided by its degrees of freedom.
*Non-parametric tests!
*the gradient of the regression line and the strength of the relationship between a predictor and the outcome variable. - 67What is the non-parametric equivalent of the Bonferroni post-hoc tests?The Games-Howell post-hoc tests.
*'no variances assumed'
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- Flashcards for all of the statistics I have learned this year in my first year of undergraduate study at UoN.
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- Deleted user · Mar 2018
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- Mar 13, 2018
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- Created by Rachael Jones · Jan 28, 2016
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