Tutorial 6:Level of significance and power of the test

The probability of making a Type I error is called level of significance (α). Normally researchers would aim to minimize the probability of making a Type I error. Most researchers will set this probability to 0.05.

The probability of making a Type II error is (β). The power of the study is calculated from (1-β) and is defined as the probability of detecting a real difference when the null hypothesis is false.

These parameters have to be predetermined by the researcher prior to the study to avert the risk of erroneously accepting the null hypothesis (even though it is really false)

Once the hypothesis, level of significance and power of the study have been fixed, the researcher can proceed to determine the statistical processes for the proper conduct and analysis of the study.

Tutorial-5:Statistical Hypothesis

After identifying and defining the variable the researcher has to develop the hypothesis about his study if his study is an experimental study. An experimental study has two hypotheses one is null hypothesis, which is a statement of no effect or no association and the alternative hypothesis is a statement, which depicts the researcher’s interests.
Two types of errors can occur while making conclusions regarding the null hypothesis: Type I error and Type II error. A Type I error refers to rejecting the null hypothesis when the null hypothesis is true (false positive). A Type II error refers to accepting the null hypothesis when it is false (false negative).

Tutorial :4 Variable classification according to their nature

Variable classification according to their nature

a. Independent variables

Variables, which are either manipulated by the researcher, or variables, which cannot be controlled by the study but will affect the results of the study, are called independent variables. Age can be independent variable under study, which may affect the outcome variable such as survival in days etc

b. Dependent variables

The outcome variables under the research problem are called as dependent variables and they will be affected by the independent variables under study. The outcome variables such as no of people developed a particular disease in a cohort study

Variable classification according to their type

a. Quantitative variables

Variables, which can be measured numerically, are called quantitative variables. These variables can be further classified as continuous and discrete variables A continuous variable could take any value in an interval Examples of continuous data are body mass, height, blood pressure, and cholesterol.

Discrete variables will whole integer values. Example: number of needle punctures and number of hospitalizations


b. Qualitative variables
Variables, which cannot be measured numerically, are called qualitative variables.

Variable classification according to the scale of measurement

a. Nominal Scale variables

These variables, which are using nominal scale of measurements, can only be classified and cannot be ordered and not suitable for mathematical treatments. Variables such as gender, name etc.

b. Ordinal Scale Variables

These variables can be ordered. However, the intervals between the numbers are not necessarily equal.

Example: Star Rating for Institutions, (Institutions can be accredited with star status like 5,4,3,2,1 star ) .Here the ordering is valid as 5 Star Rated institution is superior than 4 star rated institution.But the difference between the 5 and 4 star institutions and 4 and 3 institutions is not the same even though they differ by same number one.



c. Interval Scale Variables

When Variables measured on the interval measurement scales one unit on the scale represents the same magnitude on the characteristic being measured across the whole range of the scale. i.e. the interval between the numbers are equal. In the interval scale ratio between set of two numbers in the scale are not equal.
Temperature will be a perfect example for interval scales



d. Ratio Scale Variables

Ratio scale variables will have all the properties of interval variables and also the ratio between two numbers in the ratio scales is same.Theses scales will have a true zero.Weight and height.

Tutorial:3 Use of Biostatistical Tools in the planning stage of the research

After identifying and defining the problem clearly, researcher has decided about the type of study. Once the type of the study is decided he has to identify, classify variables before proceeding to the next step such as developing a hypothesis in case of experimental study. The variables can be classified according to their nature, type and scales of measurement i.e. independent or dependent, quantitative or qualitative, types such as nominal, ordinal, interval and ratio scales. Defining variables will have an effect on the collection and analysis part also.

Tutorial : 2. Need for Biostatistical tools

Biostatistics tools are used in research mainly because one can’t study the entire study subjects (i.e. population) because of scarcity of resources such as money, time etc. In some the research even though the resources are sufficiently available it may not feasible to conduct the research if the risk of conducting experiments on the subjects is high. When one can’t study the entire population then the he has to go for a part of the population (i.e. sample).

When the researcher studies the sample and draw inference about the population, the inference made from the sample may not be adequately represent the population and it will lead to serious error thus will defeat the purpose of the research.
Biostatistical tools help the researcher to overcome this bias and help him to draw valid conclusion about the population with defined level of confidence. Biostatistics has a role in each phase of the research .We shall start with the planning stage of research

Tutorial 1. Introduction to Biostatistics

Biostatistics tools help the researchers to take scientific decision on the research problem they are dealing with. It role starts from the planning stage of the problem to the conclusion stage. In the current scenario at least a basic knowledge about BioStatistics is essential for the researcher to succeed in their area of research. This tutorial tries to throw light on basic concepts of BioStatistics and the areas were it could be applied meaningfully with examples.

Biostatistics Definition

Statistics is defined as the science of collecting, organizing, analyzing and interpreting of data. When the statistical tools applied to the biological problems it is called as BioStatistics

A tutorial on Biostatistics will come soon.

Tutorial By
Vinaitheerthan
Biostatistician
India