Biostatistics has an important role in both designing a pharmaceutical experiment and evaluating its result. Randomization techniques are essentially important in designing an experiment.
Biostatistics has an important role in both designing a pharmacuetical experiment and evaluating its results. Randomization techniques are needed in plotting experiment. The aim of randomization is transforming organized errors into random errors and confirming comparability between experimental groups.
Pharmaceutical industry makes use of biostatistics in Drug discovery and development process, design of new drug delivery systems and formulation development, testing hypothesis in developing new drugs, formulations and for assessing the effectiveness of a drug in curing a disease.
With the help of tools of statistics, biostatisticians help to find the answers related to the research questions in medicine, biology and public health, such as whether a new drug works, what causes of diseases, and how long a person with a certain illness is likely to survive.
• Biostatistics broadly deals with statistical applications in the context of biological problems including medicine, pharmacy and public health. • Biostatistics is developed during the period of Sir Francis Galton (1822-1910) who is known as father of biometry.
Confidence Interval
The confidence interval is an estimate of the range in which the true effect of the treatment lies.
It's basically used to account for the inherent error of using the statistics of a sample population to derive conclusions about the entire treatment population.
Remember from above that it's impossible to measure the treatment effects for the entire.
Type I Error
We've hinted at this a few times above, but a Type I Error happens when the null hypothesis is true, but the researchers reject it in error.
This is like saying "This drug has an effect!" when it actually doesn't.
It's rejecting the null hypothesis when it's actually true.
You may have made this connection already, but Type I Error is related to p-.
Type II Error
In the opposite situation, Type II Error is when the null hypothesis is false, but it is accepted in error.
Put another way, a Type II Error is when there actually a difference between the two treatment groups, but the researchers say there is none.
It's accepting the null hypothesis when it's actually false.
Type II Errors can actually happen freq.
