How To Do A Data Table For Science Project

Presenting Information – Graphs and Tables

Types of Data

There are different types of data that can be collected in an experiment. Typically, we try to blueprint experiments that collect objective, quantitative data.

Objective data is fact-based, measurable, and appreciable. This means that if two people fabricated the aforementioned measurement with the same tool, they would go the same answer. The measurement is determined by the object that is being measured. The length of a worm measured with a ruler is an objective measurement. The observation that a chemic reaction in a test tube changed color is an objective measurement. Both of these are appreciable facts.

Subjective data is based on opinions, points of view, or emotional judgment. Subjective information might give two different answers when nerveless past two different people. The measurement is determined by the subject who is doing the measuring. Surveying people about which of ii chemicals smells worse is a subjective measurement. Grading the quality of a presentation is a subjective measurement. Rating your relative happiness on a scale of one-5 is a subjective measurement. All of these depend on the person who is making the observation – someone else might make these measurements differently.

Quantitative measurements gather numerical information. For instance, measuring a worm as existence 5cm in length is a quantitative measurement.

Qualitative measurements depict a quality, rather than a numerical value. Saying that one worm is longer than another worm is a qualitative measurement.

	Quantitative	Qualitative
Objective	The chemical reaction has produced 5cm of bubbles.	The chemical reaction has produced a lot of bubbles.
Subjective	I give the amount of bubbles a score of vii on a calibration of 1-10.	I think the bubbles are pretty.

Later on you take collected data in an experiment, you lot demand to figure out the best fashion to present that information in a meaningful manner. Depending on the type of data, and the story that you are trying to tell using that data, you may present your data in different ways.

Data Tables

The easiest way to organize information is by putting it into a data table. In most data tables, the independent variable (the variable that you are testing or changing on purpose) will be in the column to the left and the dependent variable(due south) will be across the tiptop of the table.

Be sure to:

Label each row and column so that the table can be interpreted
Include the units that are existence used
Add a descriptive explanation for the table

Example

You are evaluating the effect of unlike types of fertilizers on establish growth. You lot plant 12 tomato plants and split them into three groups, where each group contains four plants. To the first group, you do not add together fertilizer and the plants are watered with obviously water. The second and third groups are watered with two different brands of fertilizer. After three weeks, you measure the growth of each plant in centimeters and calculate the average growth for each blazon of fertilizer.

The outcome of dissimilar brands of fertilizer on love apple found growth over three weeks
Treatment	Institute Number
Treatment	ane	ii	iii	iv	Average
No treatment	10	12	viii	ix	9.75
Brand A	15	sixteen	14	12	14.25
Brand B	22	25	21	27	23.75

Scientific Method Review: Can you identify the central parts of the scientific method from this experiment?

Independent variable – Type of handling (make of fertilizer)
Dependent variable – plant growth in cm
Control group(due south) – Plants treated with no fertilizer
Experimental group(south) – Plants treated with different brands of fertilizer

Graphing data

Graphs are used to display information because it is easier to see trends in the data when it is displayed visually compared to when it is displayed numerically in a tabular array. Complicated data can often be displayed and interpreted more easily in a graph format than in a data table.

In a graph, the 10-centrality runs horizontally (side to side) and the Y-axis runs vertically (up and downwardly). Typically, the independent variable volition be shown on the 10 centrality and the dependent variable will be shown on the Y axis (only like y'all learned in math form!).

Line Graph

Line graphs are the best type of graph to apply when yous are displaying a change in something over a continuous range. For example, you could use a line graph to display a modify in temperature over time. Time is a continuous variable considering it can have any value between ii given measurements. It is measured along a continuum. Between i infinitesimal and ii minutes are an space number of values, such as 1.1 minute or 1.93456 minutes.

Changes in several unlike samples can be shown on the aforementioned graph by using lines that differ in color, symbol, etc.

Figure i: Modify in bubble superlative in centimeters over 120 seconds for three samples containing different amounts of enzyme. Sample A contained no enzyme, sample B contained 1mL of enzyme, sample C contained 2 mL of enzyme.

Bar Graph

Bar graphs are used to compare measurements between different groups. Bar graphs should exist used when your data is not continuous, simply rather is divided into different categories. If you lot counted the number of birds of different species, each species of bird would be its own category. There is no value between "robin" and "hawkeye", and then this data is non continuous.

Effigy ii: Final bubble height after 120 seconds for three samples containing unlike combinations of ingredients. Sample A contained enzyme simply no substrate, sample B independent substrate simply no enzyme, sample C contained substrate and enzyme.

Scatter Plot

Scatter Plots are used to evaluate the human relationship betwixt ii different continuous variables. These graphs compare changes in 2 unlike variables at once. For example, you could look at the human relationship between height and weight. Both height and weight are continuous variables. Y'all could not use a besprinkle plot to wait at the relationship between number of children in a family and weight of each child considering the number of children in a family is not a continuous variable: you tin can't accept 2.3 children in a family unit.

Figure 3: The relationship between height (in meters) and weight (in kilograms) of members of the girls softball team. "OLS case weight vs height scatterplot" by Stpasha is in the Public Domain

How to brand a graph

Identify your independent and dependent variables.
Choose the correct type of graph by determining whether each variable is continuous or non.
Decide the values that are going to go on the X and Y axis. If the values are continuous, they demand to be evenly spaced based on the value.
Label the 10 and Y axis, including units.
Graph your data.
Add a descriptive explanation to your graph. Note that data tables are titled above the effigy and graphs are captioned below the figure.

Example

Let's get back to the data from our fertilizer experiment and apply information technology to brand a graph. I've decided to graph merely the boilerplate growth for the four plants considering that is the about important slice of data. Including every single information betoken would make the graph very confusing.

The independent variable is blazon of treatment and the dependent variable is plant growth (in cm).
Type of treatment is not a continuous variable. There is no midpoint value between fertilizer brands (Make A 1/2 doesn't make sense). Plant growth is a continuous variable. Information technology makes sense to sub-divide centimeters into smaller values. Since the independent variable is chiselled and the dependent variable is continuous, this graph should be a bar graph.
Constitute growth (the dependent variable) should become on the Y axis and type of handling (the independent variable) should go on the X centrality.
Notice that the values on the Y axis are continuous and evenly spaced. Each line represents an increase of 5cm.
Notice that both the 10 and the Y axis have labels that include units (when required).
Notice that the graph has a descriptive explanation that allows the effigy to stand solitary without boosted data given from the procedure: you lot know that this graph shows the average of the measurements taken from four tomato plant plants.

Figure 4: Average growth (in cm) of tomato plants when treated with different brands of fertilizer. In that location were four tomato plants in each group (n = 4).

Descriptive captions

All figures that present data should stand alone – this means that you should be able to interpret the information contained in the figure without referring to anything else (such every bit the methods section of the paper). This ways that all figures should take a descriptive caption that gives information most the independent and dependent variable. Another mode to land this is that the caption should depict what you are testing and what you are measuring. A good starting point to developing a caption is "the event of [the independent variable] on the [dependent variable]."

Here are some examples of good explanation for figures: