GLOSSARY FOR THE SCIENTIFIC PROCESS Data – Small amount of information about the subject of an investigation. (data = plural; datum = singular) Discrete Data – Each point can be only a whole number. Cats would be discrete units because there is no possibility of a fraction of a cat. We count or tally these data. Chi-square analyses are based on discrete data. Continuous Data – Points taken along a scale that can be infinitely subdivided. Time, weight, and temperature are examples. We measure these data. Categorical Data – Each point falls into a non-numeric group or category, e.g., male or female. Distribution – The pattern of occurrence of a set of data. Class – A group of data points whose limits are set by an upper and a lower value. Used in making a frequency distribution. We divide the overall range of the values in our data set into a number of classes and count the number of data points that fall into each of these classes. Frequency Distribution – The pattern of number of measurements that fall into each class. Normal Distribution – Data in a frequency distribution spread out equally on either side of a central high point. Its appearance is that of a symmetrical bell-shaped curve with the tails of the curve extending infinitely far in both positive and negative directions. Central to probability in analytical statistics. (See below.) Experimental Design – The formal design worked out to test the prediction of a hypothesis. It designates independent variables to create so that responses in dependent variables that depend on the manipulations can be analyzed. Independent Variable – The factor to be varied by direct manipulation by the investigator or by natural categorization in the experiment. It is expected to cause an effect in the dependent variable. In a graph this variable occurs on the x (horizontal) axis. Dependent Variable – The variable whose response we measure in the experiment. It is expected to result from variation in the independent variable. In a graph this variable occurs on the y (vertical) axis. It may be a continuous or discrete variable. Treatment – One of the categories varied in a categorical independent variable.Control – A special treatment in a manipulative experiment. It is the standard, the group left unmanipulated, and provides baseline comparative data to evaluate the effect of the manipulated treatment. Item Measured/Counted – The item that is measured or counted in an experiment (e.g. a tree, a leaf, a quadrat, a population). Sampling Unit – The number of items measured/counted included in one datum point. (e.g. number per sampling unit; number of trees/quadrat; number of seeds/dish) Replicates (N) – The number of sampling units in each treatment. Experimentation – Methods used to test predictions of hypotheses. Manipulation – Alterations in the independent variable are created by the investigator. Observation – Natural variation in the independent variable occurs, requiring no alteration, only direct observation of the dependent variable by the investigator. Measurement – Performed on continuous variables. Count or Tally – Performed on discrete and categorical variables. Graph (Figure) (Chart) – A diagram that represents the variation of a variable in comparison with that of one or more other variables. Axes – Horizontal (x axis, abscissa) for independent variable(s). Vertical (y axis, ordinate) for dependent variable(s). Bar Graph – Used when the independent variable is categorical or divided into classes (otherwise known as Column Graph). Box and Whisker Graph – Used to display differences among treatments in means, ranges, and standard deviations. Column Graph – (see Bar Graph). Histogram – Used to display frequency distributions. Classes of measurement occur on the x axis and frequency in each class on the y axis. Line Graph – Used when the x axis represents a continuous variable. Sometimes the x variable is the independent variable. Other times, as in showing a correlation, neither x nor y variables are designated as independent or dependent variables. Hypothesis – A formal statement of a possible explanation for an observed phenomenon. A “might-be” about the way the world works. It leads to predictions.Null Hypothesis – A statistical hypothesis stating that there is no association between two variables or no difference among means. (e.g. H0: A=B) Alternative Hypothesis – A statistical hypothesis stating the pattern in the data that is expected if the predictions holds true. (e.g. HA: A>B) Speculation – A first informal attempt at explaining an observed phenomenon. Prediction – A consequence expected by the logic of the hypothesis. An experiment arises out of the predictions. If…,then logic – A formal conditional statement of the hypothesis and prediction that uses deductive logic. If the phenomenon I observed can be explained in this way, then these consequences should occur. The “if” clause contains the hypothesis and the “then” clause the prediction that is to be tested. Cause…Effect – In a manipulation experiment, we test whether the independent variable causes an effect (response) in the dependent variable. Testability – The hypothesis must be susceptible to testing through the scientific process, where science is limited to the study of the physical world. Assumption – A fact that is taken-for-granted in the experiment. If the experiment fails to falsify the hypothesis, the assumption may not have been true and now itself would need to be tested. Population – Any set of individuals or objects having some common observable characteristic. The unit from which the data sample is taken. Sample or Sample Set (N) – The sub-set of the population measured or counted in the experiment. Random Sample – A sample taken with no bias. Scientific Process or Method – The logical process by which scientific information is gathered by asking and answering questions about the physical world. Statistics – A tool used 1) to describe trends and relationships and 2) to decide whether to accept or reject an hypothesis based on the probability of whether the results of an experiment could have occurred by chance or not. Descriptive Statistics – A summary of data in a variable that provides information about its central tendencies and dispersion. Parameter – A