All of us are familiar with statistics that we see every day: statistics on sport, weather and stock market. The sort of thing that could appear in The Age or The Sydney Morning Herald’s number crunch.
Statistics is all about data. It’s the art of designing methods to gather, summarise and visualise data, as well as present inferences.
The fundamental concepts of variability and uncertainty are the basis of all that is possible. To help you understand the vast array of statistics we encounter, I will break it down into three categories.
First, statistical facts. These facts can be interesting by themselves or when compared with other facts for different times and places. These facts may seem like data but they are actually summaries.
What the percentage of Australians age 15 or over who not marry in 2001? It is 32%. The 2001 Australian Census is behind the 32% figure. It is a summary of Census data and has no statistical uncertainty.
Data collected from surveys of populations using randomly selected units are more complex. Variability is a factor as different samples can give different results.
Summarized As Percentages Statistics
The results of sample survey data are usually summarize as percentages, proportions or averages. These inferences concern the population. Because of sampling variability error or other types of error, they will not be exact inferences.
The 2007 National Survey of Mental Health and Wellbeing ABS revealed that approximately 3.2 million Australians (20% of those aged 16-85) suffered from a mental disorder during the 12 months preceding the survey.
(This article is not intend to discuss the uncertainty inherent in estimates like this one or how it is interpret. Let’s call it the second category of sample survey statistics.
These survey results, such as the one in our example, are crucial if we want to make informed decisions in order to realize our aspirations.
Let me now turn to the third category, which is the vast array of other uses for statistics. This includes professionals who use statistics statisticians, and those whose primary activity is not statistics.
Subcultures Of Statistical Statistics
Statistics are use in distinctive ways by different sectors of the economy, such as agriculture.
However, if you search the internet with key phrases like statistics on agriculture, you’ll most likely find statistics about agriculture and not how statistics can improve agricultural practice. This requires a more target search, but statistics can be use in many different ways.
Helen Newton Turner (1908-1995), a long-serving member of the CSIRO, applied statistics to animal production, later to sheep breeding. Modern versions of her research are still in use today.
Comparative crop variety testing is the work of statisticians, which provides information about variety performance for Australian farmers. Statistics are also use by the mining, manufacturing, and service industries.
It is difficult to name any area of science, technology or industry, government, or even the humanities that does not have its own subculture of statistical data its own type of data, its own collection of questions, models, and methods to answer these questions using the data and a statistical literature.
Statistics is evident in every area of human endeavor. This can be done through statistical facts, sample-population analysis, or any other informal or formal method of answering questions of interest. This statistical activity does not have to be done by statisticians, as I will show.
All Aspects Of Health Are Affect By This Role
As a guide to where we want to go, we might start with summary statistics about mortality and morbidity. Surveys can be use to get more information about specific topics than what we have from our routine data collection.
One example is the mental health survey. Biostatistics and epidemiology work together to identify risk factors and understand disease patterns.
Biostatistics can use to design, conduct, and analyze clinical trials in order to evaluate the effectiveness of vaccines, drugs or devices, as well as treatments.
It is widely use in preclinical biomedical research where cells lines and model organisms are analyze in order to conduct the necessary studies before a drug is approve for human clinical trials.