Biology Graphing Notes

AP Biology Graphing Notes Graphing is an important procedure used by scientists to display the data that is collected during a controlled experiment. When a graph is put together incorrectly, it detracts the reader from understanding what you are trying to present. Most graphs have 5 major parts: 1. The Title depicts what the graph is about. It gives the reader an understanding about the graph. 2. The Independent Variable (X-axis) is controlled by the experimenter. Remember this is the variable that “I” am testing (I for Independent). Common independent variables include: time, generations, measurements (length, distance), and temperature. This variable goes on the X-axis. 3. The Dependent Variable (Y-axis) is affected by the independent variable. This variable is what the experimenter measures. This variable goes on the Y-axis. 4. Develop a Scale for each variable. Before you can plot your data points, you must figure out how much each box on your graph paper is worth. Scale doesn’t’ always have to start at zero, but it must be consistent. If you start off making each box worth 5 cm, each subsequent box must also be 5 cm. Always make sure your scale is labeled with what it is and what the units are. 5. Create a Key, a short description about the graph’s data. Most often used to show what different patterns or colors stand for on your graph. Rules and Tips for Graphing: 1. Always use a pencil to draw your graph. It’s easier to fix mistakes (Or use Excel!). 2. Always draw lines with a ruler. Do not freehand. Use at least half of your paper for the graph. 3. Make sure Independent Variable is on the X-axis and Dependent Variable is on the Y axis. 4. Include all parts! 5. If you are graphing multiple subjects, use different colored or patterned lines and explain what they are in the legend. Which Graph Type To Use?  Line Graph: are used for looking at the relationship between two continuous types of data. Typically, both the independent and dependent variables are numerical.  Bar Graphs: are used for making comparisons between discrete cases or to look for trends, such as over space or time. The independent variable is usually a category and the dependent variable is usually an average, percentage, or frequency.  Scatter Plot: Scatter plots are used for examining relationships between two types of data. These are very similar to line graphs, just without the line. Line graphs provide an excellent way to map independent and dependent variables that are both quantitative. When both variables are quantitative, the line segment that connects two points on the graph expresses a slope, which can be interpreted visually relative to the slope of other lines or expressed as a precise mathematical formula. Scatter plots are similar to line graphs in that they start with mapping quantitative data points. The difference is that with a scatter plot, the decision is made that the individual points should not be connected directly together with a line but, instead express a trend. This trend can be seen directly through the distribution of points or with the addition of a regression line or line of best fit. Determining the rate from a graph. The AP Biology exam often asks students to find the rate of a process or reaction between two points on a graph. In order to find the rate, calculate the slope of the best fit line that connects the two points. Use Rate = (y2-y1) / (x2- x1) Interpreting Error Bars on Graphs What is an Error Bar? An error bar is a line through a point on a graph, parallel to one of the axes, which represents the uncertainty or variation of the corresponding coordinate of the point. In Biology, the error bars most often represent the standard deviation of a data set. Why Include Error Bars on a Graph? Error bars can communicate the following information about your data:  How spread the data are around the mean value (small SD bar = low spread, data are clumped around the mean; larger SD bar = larger spread, data are more variable from the mean).  The likelihood of there being a significant difference between between data sets. More on this below... What do Error Bars Indicate about Statistical Significance? A "significant difference" means that the results that are seen are most likely not due to chance or sampling error. In any experiment or observation that involves sampling from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if result is "significant," then the investigator may conclude that the observed effect actually reflects the characteristics of the population rather than just sampling error or chance. The standard deviation error bars on a graph can be used to get a sense for whether or not a difference is significant. Look for overlap between the standard deviation bars: When standard deviation errors bars overlap quite a bit, it's a clue that the difference is not statistically significant. You must actually perform a statistical test to draw a conclusion. When standard deviation errors bars overlap even less, it's a clue that the difference is probably not statistically significant. You must actually perform a statistical test to draw a conclusion. When standard deviation error bars do not overlap, it's a clue that the difference may be significant, but you cannot be sure. You must actually perform a statistical test to draw a conclusion.