BCPS Statistics Questions and Answers 100% Accurate

BCPS Statistics Questions and Answers 100% Accurate NOIR Data types: nominal, ordinal, interval, ratio Nominal Yes or No variables or sex, mortality, dz presence, race, marital status Ordinal Ranked in order but no magnitude of ranks. Ex: NYHA, scale *means and SD should not be reported Interval Data ranked in order. Zero is arbitrary. Points can pass zero. Ex: Fahrenheit temperature Ratio Measurables and continuous. The values don't cross zero. Ex: HR, BP, time, distance Visual methods of describing data (3) Frequency distribution, histogram, scatterplot Measures of central tendency Mean, median, mode Mean Generally used for continuous and normally distributed data Median Midpoint of values when placed in order from highest to lowest *ordinal or continuous data Mode Most common value * used for nominal, ordinal or continuous *does not help describe meaningful distributions with a large range of values each occurring infrequent 2 types of discrete variables Nominal and ordinal Nominal data Unordered data. Ex-sex, mortality, disease presence, race, marital status Ordinal data Ranked in "order" but no consistent level of magnitude of difference between ranks. 1 and 2 in the hf scale doesn't match up as the regular number 1 and 2. The interval between the numbers doesn't mean the same thing. Common error with ordinal data Means and SD should not be reported Interval scale Data ranked in a specific order with a chance consistent change in magnitude between units. The zero point is arbitrary. Ex-degrees Fahrenheit. You can have a negative temperature Ratio scale Interval but with an absolute zero -degrees kelvin -age -hr -BP -time -distance Visual methods of describing data (3) -frequency distribution -histogram -scatterplot Numerical methods of describing data (3) -mean -median -mode Mean used for what type of data? -continuous and -normally distributed Median AKA 50th percentile Median used for what type of data? -ordinal or -continuous -especially good for skewed populations -insensitive to outliers Continuous variables AKA and types of -counting variables -interval & ratio Mode used for what types of data? -most common Value in a distribution -nominal, ordinal or continuous Standard deviation is Most common measure used to describe a spread of data Standard deviation data has to be... -continuous -normally distributed Variance equation Standard deviation squared Coefficient of variation meaning Relates the mean and the SD Coefficient of variation equation SD/mean * 100% Standard deviation sample values 68% of the sample values falls in 1 SD, 95% in 2 SD, and 99% in 3 SD Variance describes The variability Range -The difference between the smallest and largest value in a data set. -does not provide a lot of info -sensitive to outliers 3 ways to measure variability -standard deviation -range -percentiles Percentiles and population reporting -iqr What does iqr encompass? -the 25th to 75th percentile Which measure of central tendency should NOT be used for ordinal data? -means and sd should not be used Best measure of central tendency for ordinal data Median and IQR Best measurement of central tendency for continuous data Means and SD How to tell if data normally distributed? Eyeball it or if mean and median look close its likely it is Formal test to tell if data is normally distributed Kolmogorov-Smirnov test Another indicator data isn't normally disttibuted If the SD is larger relative to the mean it's likely it isn't normally distributed Parametric data Mean and SD parameters that define a normally distributed population SEM Quantifies the uncertainty and the estimate of the mean SEM calculation SD/square root of the n Why do you need to calculate SEM? -calculate confidence intervals -hypothesis testing -deception Does the CI and the p-value need to be reported? No Is a CI that includes zero statistically significant? No Null hypothesis (Ho) No difference between groups being compared. If this is rejected there is a statistically significant difference Alternative hypothesis (Ha) Opposite of null hypothesis. It is stated there is a difference Parametric test assumptions -normally distributed -continuous data -variances are approximately equal Non parametric tests -Data is not normally distributed -discrete data Student t test-parametric or non parametric -parametric Student t test -compares study sample with the known population mean Ex: ldl samples of meeting goers versus the population average 3 types of t tests -Student t test -2 sample, independent sample or unpaired test -paired t test 2 sample, independent or unpaired test Compared the means of two independent samples Paired t-test Compares the mean difference of paired or matched samples -2 samples from each person at a different time period Paired t-test parametric or non parametric test Parametric Anova -compares the means of three or more groups in a study Anova test parametric or non parametric test Parametric Anova limitations -we only know one of the groups are different when the p value is statistically significant. We don't know which one of the three is different, post hoc analysis needs to be done Post hoc done -after Anova shows a statistically significant difference Post hoc tests (4) -Tukey HSD -bonferroni -scheffe -Newman-keuls Non parametric tests -wilcoxon rank sum or Mann Whitney U (same test) -Kruskal-wallis one way Anova Non parametric tests, type of data and distribution -ordinal or counting data -data not normally distributed Wilcoxon rank sum/Mann Whitney u test -2 independent samples -not normally distributed -related to the t-test Kruskal-wallis one way Anova -related to one way Anova -not normally distributed data Test done after Kruskal-Wallis for post hoc testing Wilcoxon rank Sum test