Null hypothesis: statement of no difference

Alternate hypothesis: definite difference between groups of a subject

Statistical analysis: Organisation and interpretation of data according to well defined, systematic, and mathematical procedures and rules

Levels of statistical analysis: descriptive stats, inferential stats, associational stats, difference

Descriptive statics: data reduction

Inferential statistics: inferences to known population

Associational statistics: causality

Difference: difference between two or more groups of data

Descriptive statistics types: MCT, measures of variability, bivariate descriptive statistics

Measures of central tendency: mode, median, mean

Measures of variability: range, IQR, sum of squares, variance, standard deviation

Bivariate descriptive statistics: contingency tables and correlational analysis

Normal distribution: both halves are identical

Nonsymmetric: either positively (point towards the left) or negatively (point towards the right) skewed

Kurtosis: distribution characterised by its shape

Correlational analysis: determines relationships among variables

Level of significance: statement of the expected degree of accuracy of the findings based on sample size

inferential statistics – processes for drawing conclusions about a population based on data

confidence interval and level: range of values observed in sample that accurately reflects the population

significance level: indicates whether the samples being tests are from the same or different population

one-tailed and two-tailed level of significance: two extreme scores of the bell curve

type I error (alpha): researcher fails to accept null hypothesis

type II error (beta): accepts null hypothesis when it should be rejected

parametric statics: formulas that test hypothesises based on three assumptions

parametric stats assumptions: sample is serviced from population with a normal distribution, variance if homogeneous, data measures at interval level

t-test: Compare the difference of means of one variable

one-way analysis of variance (ANOVA): Test the difference between the means of two or more variables

pearson: measures the strength and direction of a linear relationship between the x and y variables

non-parametric statistics: test hypothesis when the data violates one or more of the assumptions of parametric procedures

chi-squared test: when data is nominal, and computation of mean is not possible

mann-whitney test: test differences between two independent groups

Wilcoxxin signed test: nonparametric alternative to t-test for correlated samples

Kruskal-wallis test: non-parametric alternative to ANOVA

Kappa: determine the degree of agreement between two or more judges

Hawthorne effect: attention given to subjects increases positive outcome

Placebo effect: receiving treatment may produce positive expectations

Honeymoon effect: a short-term effect of a new treatment procedure subjects hope will impact on the disease

Research bias: researcher affecting results through enthusiasm and interest for the treatment to be effective

Test administer bias: person aware which subjects get treatment and which don’t

Sampling error: sample not representative of population

Systematic variance: researcher fails to control extraneous variables such as age, race, gender

Error variance: researcher overlooks unexpected problems in test environment