1. What is the difference between descriptive and inferential statistics? Give an example of each. - Descriptive statistics summarize the characteristics of a data set, such as mean, median, mode, standard deviation, frequency, etc. Inferential statistics use the data to make generalizations or predictions about a population or a hypothesis, such as confidence intervals, t-tests, ANOVA, regression, etc. An example of descriptive statistics is calculating the average age of patients in a hospital ward. An example of inferential statistics is testing whether there is a significant difference in blood pressure between smokers and nonsmokers. 2. What are the types of variables in biostatistics? How are they measured? - Variables are the characteristics or attributes that can vary among individuals or groups. There are two main types of variables: categorical and numerical. Categorical variables have values that belong to a finite set of categories, such as gender, blood type, diagnosis, etc. They are measured by counting the frequency or proportion of each category. Numerical variables have values that are numbers that can be ordered or measured, such as height, weight, temperature, etc. They are measured by calculating the central tendency (mean, median, mode) and dispersion (range, variance, standard deviation) of the data. 3. What is the difference between parametric and non-parametric tests? When are they used? - Parametric tests are statistical tests that assume that the data follow a certain distribution, such as normal, binomial, Poisson, etc. They also require that the data meet certain criteria, such as homogeneity of variance, independence of observations, etc. Non-parametric tests are statistical tests that do not make any assumptions about the distribution or the criteria of the data. They are based on ranks or signs of the data rather than the actual values. Parametric tests are used when the data meet the assumptions and criteria of the test, and when the sample size is large enough to ensure that the sampling distribution is approximately normal. Non-parametric tests are used when the data do not meet the assumptions or criteria of the test, or when the sample size is too small to ensure normality. 4. What is a confidence interval? How is it interpreted? - A confidence interval is a range of values that estimates a population parameter with a certain level of confidence. It is calculated from the sample statistic and its standard error, and it depends on the chosen confidence level (usually 95% or 99%). A confidence interval is interpreted as follows: if we repeated the sampling process many times and calculated a confidence interval for each sample, we would expect that a certain percentage (the confidence level) of these intervals would contain the true population parameter.