- Research Hypothesis
- Types of Variables
- How do we describe a data set?
- What is a skewed distribution? and a normal distribution?
- Sample Standard Deviation or Population Standard Deviation?
- How can we compute the Coefficient of Variation?
- What is the Confidence Interval and how to compute it?
- What is the Standard Error and how to compute it?
- What are the Type I and Type II errors in biostatistics?
- Tips to know which statistical test we can use
- Examples of Study Designs and Inferential Statistics
- Some comments about simple linear correlation
- Post-hoc tests

Usually, the hypothesis that you support (your prediction) is the alternative hypothesis (HA), and the hypothesis that describes the remaining possible outcomes is the null hypothesis (H1).

Nominal variables are qualitative variables without order (only categorization is possible). For instance, 'Color' is a nominal variable that can have several several categories (e.g., red, green, blue, etc.). Another Nominal variable is 'Gender' (with two or more categories: male, female, others).

Ordinal variables are qualitative variables with some sort of order. For instance, 'Satisfaction' can be an ordinal variable with the following five categories: 'Bad', 'Not so bad', 'Normal, 'Good', 'Very Good'. As you can see, there is some order implied, 'Good' is better than 'Normal' which in turn is better than 'Not so bad'.

Quantitative variables can be represented by any real number. Some Statistical Softwares such SPSS distinguish between Interval and Ratio variables, both are quantitative. Interval variables don't have a fixed origin, they have a fixed distance but not a fixed origin. For example, Temperature in Celsius Degrees. Ratio variables do have a fixed origin. For instance, Time is a Ratio variable because it has an absolute origin (time = 0 seconds).

In other words, if our variable is Quantitative and our distribution is not skewed, we will use the Mean as a metric for central tendency and the standard deviation as a metric for dispersion. However, if our distribution is skewed, the right metrics will be Median for central tendency and Interquartile Range for dispersion.

If our variable is Ordinal, the only central tendency metric that we have to use is the median, and the only dispersion metric we should use is the Interquartile Range.

Finally, if our variable is Nominal, we should describe the dataset by means of the mode (i.e., most frequent number).

There is a rule of thumb, if |skewness statistic|<2·|standard error|, our dataset can be considered symmetrical. Usually, skewness and standard error can be easily obtained using Statistical Softwares such SPSS.

We should use the Sample Standard Deviation especially when we have a sample size smaller than 75. Both types of Standard Deviation are computed differently. The Sample Standard Deviation has what it's called 'Bessel's Correction', which is the 'n-1' shown in the figure below.

The figure below show its computation.

Let's assume we found in our experiment a 95% Confidence Interval of [23, 31], if we repeat the experiment 100 times with a similar sample, in 95 out of the 100 times the estimate value will fall inside the Confidence Interval [23,31].

The figure below show its computation.

It's not equivalent to the Standard Deviation. They are different concepts.

The figure below show the Standard Error formula.

**STEP 1:**Think of your research question. ¿are you looking for association, relationship and/or group prediction?**STEP 2:**Identify Independent variables (IV), Dependent variables (DV) and Covariates (CV).**Dependent variable:**what you really measure (e.g., weight, height, satisfaction,…).**Independent variable:**how you classify the measurement. This can be in a 'related (or paired)' or 'independent (unpaired)' way. These are typically grouping variables (e.g., time, treatment, …).**Covariates:**variables that we think are affecting (somehow) the outcome of the study.

**STEP 3:**Identify data type of each variable (Nominal, Ordinal, Quantitative). Identify number of levels of nominal and ordinal variables.**STEP 4:**Take a look at the magic table.

Use

Note that the Determination coefficient is the amount of variance of Y that can be explained by the variance of X.

When we analyze the coefficient of determination (r

Use