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What Is Statistics and Why It Is Important to Health Sciences? Introduction When people hear the word statistics,

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many think of numbers. Statistics is so much more than that. Statistics is a collection of methods for planning experiments, obtaining data, and then organizing, summarizing, analyzing, interpreting, presenting, and drawing conclusions based on the data” (Triola, 2010). Statistics starts from the moment a question is formed or an idea needs testing. Statistics Statistics has multiple components to its definition. Statistics are commonly used in planning experiments. Experiments need to be conducted in a manner that prevents bias from being introduced, and in a way that allows the results to be applied to the desired population. A couple of important words have now been introduced: bias and population. So, what is bias? Bias occurs when someone either intentionally or unintentionally imposes an opinion into an experiment. This would make the results useless. Population is another important concept. A populationis the complete collection of all elements of interest. The most common thought when discussing a population, is the population of the United States or a particular location. A population in statistics can be something different. Your population could be as broad as every person in the world with cancer, or narrowed considerably to patients with pancreatic cancer. The population of interest could even be the tumors themselves. Data Once you determine your population, it is time to gather some data. Data observations (such as measurements, genders, survey responses) that have been collected and have meaning attached to them (Triola, 2010). There are two different types of data, qualitative and quantitative. Quantitative data consists of numbers representing counts or measurements. Qualitative data can be separated into different categories that are distinguished by some non-numeric characteristic (Triola, 2010). In order to gather this data you will need a sample. A sample is a subset of the population that is representative of the population. In order to be representative, the sample must be collected properly. The Visual Leaner: Statistics describes several sampling techniques: Random Sample: Members from the population are selected in such a way that each individual member has the same chance of being selected. • Simple Random Sample: Each sample of size n is selected in such a way that every possible sample of size n has the same chance of being selected. • • Systematic Sampling: Randomly selects a starting point, and then every kth element. • Convenience Sampling: Collects results that are easiest to obtain. Stratified Sampling: Subdivides the population into at least two different groups called strata that share the same characteristics. As sample is then drawn from each group. • Cluster Sampling: Divides the population area into sections (clusters), then randomly selects clusters and chooses all the members of those clusters. • Frequency Distribution After the data has been collected, a discussion occurs on how to display the data. For discrete data, or grouped continuous data, the frequency or number of representatives in each group is determined and presented as a frequency distribution (Triola, 2010). Thedistribution. The number of individuals (or frequency) in each group or category is represented by the height of a bar. Line plots are used for general guidelines for scientific publications or to display continuous data. Bar charts or pie charts are used to present discrete or categorical data. A histogram is used for grouped continuous data. Though a histogram and a bar chart may look somewhat alike, the bar chart has spaces between the bars which indicate discontinuity in the data. The bars in a histogram touch neighboring bars, indicating grouped continuous data (Triola, 2010). There is an example available in the Visual Learner: Statistics. Displaying the data is important but one needs to more completely describe the data. Descriptive statistics are used to describe the data. Descriptive statistics include measure of center and variability. Central Tendency A measure of center is a value that describes the center or middle of the data set. There are examples of finding the mean, the median, and the mode in the Visual Learner: Statistics. Notice that the mean is different for the population and for samples. Variability The measure of variability is a value that describes the spread of the data. There are examples of finding the mode, the variance, and the standard deviation in the Visual Learner: Statistics.Notice that the calculations for the variance and standard deviation are different for the population and for samples. Conclusion This lecture discussed planning experiments, how to obtain data, organizing, and summarizing the data. To ensure complete description of the data both a measure of center and a measure of variability are required. The analysis, interpretation, presentation, and conclusions based on the data will be discussed in future topics. References Triola, M. (2010). Elementary statistics (11thed.). Boston, MA: Addison Wesley.
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