SportsTurf provides current, practical and technical content on issues relevant to sports turf managers, including facilities managers. Most readers are athletic field managers from the professional level through parks and recreation, universities.
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8 SportsTurf | April 2014 www.sportsturfonline.com Facility & Operations | By Chad C. Follis and Brad S. Fresenburg W e are surrounded by numbers every day. You may not realize it, but statistics plays a large role in our daily lives as well. Weather forecasting takes numbers and makes pre- dictions about the weather based on weather models. Disease models for predicting turfgrass diseases do a similar service. Based on numbers related to temperature, hu- midity and leaf wetness, these models can forecast the startup of a turfgrass disease. We know that pest control products are tested for their effectiveness to control pests. Statistics are behind every medical study and batting average you hear about. Soon we will be bombarded with those political voter polls. Statistics are sets of mathematical equations that are used to analyze what is happening in the world around us. It is a sci- ence of decision making. It is a science of "chance" or "proba- bility." It is the science of collecting, organizing, and interpreting data whether it is numerical or non-numerical. We live in an information and technological age where we have everything at our finger tips. H.G. Wells, the father of science fiction, predicted that statistical thinking would be as necessary for daily living as reading and writing. Statistics may seem in- timidating at first, but it is not once you develop a clear under- standing of this simple subject. BASIC UNDERSTANDING OF TERMS Before we start, a discussion and understanding of some basic terms are needed. Descriptive statistics[ are used to describe sets of numbers such as plants heights achieved due to applications of fertilizers. Researchers can organize these numbers into tables and graphs called requency distributions (the frequency a number may occur due to a factor involved). The following data set illustrates measurements of plant heights in centimeters after a fertilizer ap- plication). We will use this data to help us define some terms. As we look that this simple data set, we can determine a median, a mean, and a standard deviation. The median is the measurement that lies in the middle of the data, at the 50th percentile. In this example, it is 12 (range is 7-16). At times, it is better to express the median rather than the average (also known as the mean, see below), especially if the data contains outliers. The median could be a better indicator of true center especially when NBA salaries are being discussed. The mean is simply the average (plant height x frequency observations = 286 cm / 25 frequency observations = 11.44 cm) for the data set. The standard deviation (SD = 2.38) indi- cates the average difference individual data varies from the mean; how concentrated the data are around the mean. So why is this important? Without standard deviation, you cannot get a feel for how close the data are to the mean or whether the data are spread out over a wide range. Without standard devia- tion, you cannot compare two data sets effectively. Two data sets can have the same mean, but vary greatly in the concentra- tion of data around the mean; therefore different standard de- viations. The distribution of a data set can be a graph of all values and their frequency of occurrence. One of the most common distributions is called the normal distribution or bell-shaped curvedisplaying numerical data in a symmetrical curve. WHAT DID THAT PROFESSOR SAY? Statistics made easy 10 14 11 12 15 15 12 13 14 13 12 8 12 9 10 13 11 12 8 10 9 16 7 11 9 Plant Heights (cm) due to Fertilizer Applications 7 1 4 4 8 2 8 12 9 3 12 24 10 3 12 36 11 3 12 48 12 5 20 68 13 3 12 80 14 2 8 88 15 2 8 96 16 1 4 100 Totals 25 100 Plant Height (cm) Frequency Percent Percentile