Statistical analysis is fundamental to all experiments that use statistics as a research methodology. Most experiments in social sciences and many important experiments in natural science and engineering need statistical analysis.
Statistical analysis is also a very useful tool to get approximate solutions when the actual process is highly complex or unknown in its true form.
Example: The study of turbulence relies heavily on statistical analysis derived from experiments. Turbulence is highly complex and almost impossible to study at a purely theoretical level. Scientists therefore need to rely on a statistical analysis of turbulence through experiments to confirm theories they propound.
In social sciences, statistical analysis is at the heart of most experiments. It is very hard to obtain general theories in these areas that are universally valid. In addition, it is through experiments and surveys that a social scientist is able to confirm his theory.
What is the link between money and happiness? Does having more money make you happier? This is an age-old question that scientists are now trying to answer. Such experiments are highly complex in nature. After various studies, it turns out that there is a direct relationship between money and happiness till you reach a certain income level that corresponds to minimum basic requirements of food, shelter and clothing and after this level (it is about $60,000/year in the US), money and happiness seem independent of each other.
Students of science need to know statistical analysis as so many areas use it. There are also many pitfalls to avoid. Statistics can be used, intentionally or unintentionally, to reach faulty conclusions. Misleading information is unfortunately the norm in advertising. The drug companies, for example, are well known to indulge in misleading information.
Knowledge of statistics therefore will help you look behind the numbers and know the truth instead of being misled to believe something that is not true. Data dredging is another huge problem especially in this internet era where numbers and data are so easy to come by. Only by knowing the robust elements of statistical analysis can one really harness the potential of this incredible tool.
Survey questions are another favorite area that can very easily be manipulated. This happens all the time, right from presidential election surveys to market surveys by corporations. It can always happen unintentionally, which means you need to be even more careful. Such bias is hard to detect because it doesn’t come out easily in the statistical analysis and there is no mathematical technique that will determine whether this question is biased.
It is therefore important that you understand not just the numbers but the meaning behind the numbers. Statistics is a tool, not a substitute for in-depth reasoning and analysis. It should supplement your knowledge of the area that you are studying.