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**Parametric hypotheses tests**

Marek Majdan Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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**Principle of hypothesis tests**

Null and alternative hypothesis Statistical tests used to reject or accept these hypotheses and infere results from a sample to the population Every test has his own hypothesis Choice of test depends on data Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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**T test One sample (compares sample mean with population mean)**

Two independent samples (compares means of two samples) Paired (compares means of repeated measurements in the same sample) Null hypothesis: compared means are equal Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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T test in R 2 independent groups t-test t.test(y~x) - where y is numeric and x is a binary factor 2 independent groups t-test t.test(y1,y2) - where y1 and y2 are numeric Paired t-test t.test(y1,y2,paired=TRUE) - where y1 & y2 are numeric One samle t-test t.test(y,mu=3) – where mu is the population mean Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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**ANOVA test To compare means of three or more groups Null hypothesis:**

the compared means are equal In R: summary(aov(variable~grouping variable, data=database name)) Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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Post hoc test Anova only tells us that at least two of the compared means are equal Post hoc test compares mean of each group with each Tukey honest significance test results=TukeyHSD(aov(ar_lv~sampleNO, data=zoltan));results Other tests: paired t test with corrections Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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**Comparing proportions**

Chi squared test to compare proportions between categories of a 2x2 table, 2xN table or MxN table In R: chisq.test (table) prop.test (table) Training in essential biostatistics for Public Health Professionals in BiH, Marek Majdan, PhD;

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Hypothesis Testing Steps of a Statistical Significance Test. 1. Assumptions Type of data, form of population, method of sampling, sample size.

Hypothesis Testing Steps of a Statistical Significance Test. 1. Assumptions Type of data, form of population, method of sampling, sample size.

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