Bias

Bias & Confounding

Will this research produce valid findings?

This is in all probability the most alpha question to address when designing a research project. But how can you en convinced(predicate) that your research impart be valid? Techniques such as blinding and randomisation can enhance validity, but they do not see to it validity and they may be inappropriate or screwball for your depicted object. So there is no substitute for making sure that you understand how validity may be compromised and design your study accordingly.



Threats to validity

There atomic number 18 broadly three reasons why findings may not be valid-

1) Chance

2) Bias

3) Confounding

Chance

The measurements we make while doing research be nearly always subject to random variation. Determining whether findings are due to chance is a key feature of statistical analysis. Check our statistics links to find out more rough hypothesis testing and estimation. The best way to avoid computer error due to random variation is to ensure your sample coat is adequate.

Bias

Whereas chance is caused by random variation, bias is caused by self-opinionated variation. A systematic error in the way we read our patients, measure our outcomes, or analyse our data will widen to results that are inaccurate.



There are numerous types of bias that may effect a study. Understanding how bias occurs is more important than remember the names of different types of bias

Types of bias

These can broadly be divided into three categories -

1) Selection bias

The selection of subjects into your sample or their allocation to treatment group produces a sample that is not representative of the population, or treatment groups that are systematically different. stochastic selection and random allocation are the keys to avoiding this bias.



2) measuring stick bias

Measurement of outcomes is inaccurate. This may be due to inaccuracy in the measurement...If you want to tie a full essay, order it on our website: Orderessay



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