• Assess students as they work through the Experimental Analysis worksheet.
• Assess students as they brainstorm during the introduction activity.

Inferences versus Observations

To prepare for this lesson, make a homemade candle from a potato. You are trying to make a piece of potato look like a small white candle from a distance:

Handmade “Candle” Recipe

1.      Cut a cylinder out of a white potato to look like a candle, which will fit into an empty metal tea-light candle holder.

2.      Fit it into an empty metal tea-light candle holder.

3.      Using an unwound paper clip, poke a hole through the potato (lengthwise) and place a small wick in the hole. Use the wick from the tea-light candle the metal base was taken from.

Hand out the Experimental Analysis Worksheet–Student Version (S-7-6-3_Experimental Analysis Worksheet-Student Version.doc). Say, “Working in groups of two, you are to light the item given to you (it will be a candle… just don’t use that word) and watch it burn for 5 minutes. Complete Part 1 of the Experimental Analysis worksheet.”

After students record their observations, ask them to take their seats. Call on students, asking them to give sample observations. Generate a list on the board of at least 15 observations. Take your handmade “potato candle” and light it. Let it burn for 30 seconds so the class thinks it is a candle. Blow the flame out and take a bite from the potato. Students will think you are biting a piece of wax!

Say, “Look at the list of observations we generated. Are there any that we should change? Think about what an observation really is.”

At this point, some students may realize that they made an assumption instead of an observation about the composition of the “candle.” Say, “Did you know that some of the items on our list are actually inferences? What is the difference between an observation and an inference?”

An observation:

• usually uses five senses
• states fact(s)
• describes something as it appears

An inference:

• involves a decision about what you are observing
• is sometimes an opinion
• can be an interpretation based on prior knowledge or experience

Say, “How many of you referred to the wax melting? Is that an observation or an inference? It is an inference because you do not know the burning object is made from wax. You inferred it was made from wax based on prior experience. Go through your list, and place an O next to the true observations and an I next to the inferences.”

At this time, have students complete Part 2 of the Experimental Analysis Worksheet–Student Version (S-7-6-3_Experimental Analysis Worksheet-Student Version.doc
, and S-7-6-3_Experimental Analysis Worksheet-Teacher Version.doc).

Interpreting Data/Graphs

Say, “The data you collected during the Rocket Lab was a function of direct observations and measurements. You also made a graph of that data. Now that we have learned more about observations and interpretations, you are able to interpret your data/graph and make conclusions about your results.”

“Your line of best fit and the equation of the line can be useful in making further conclusions. For instance, you did not do a trial with 8 pennies, but you can use the equation of the line to determine how high the rocket would go with 8 pennies (according to your other data). This is called extrapolating data. How do you do this? Look at your line equation:”

Example:

y = (–15) x + 135

“Would 8 pennies be the x or the y value? On which axis was the number of pennies placed? (the x-axis) Therefore, 8 can be plugged into the x spot in the equation. If you solve for y, you will determine how high the rocket would reach with 8 pennies.”

Example: y = (–15)8 + 135

y = –120 +135

y = 15 cm

Have students calculate how high their rocket would have gone, according to their data, with 0 pennies, 1 penny, and 8 pennies on the Experimental Analysis Worksheet Part 3 (S-7-6-3_Experimental Analysis Worksheet-Student Version.doc).

Error Analysis

Say, “One of the most important aspects in analyzing your experimental data and forming conclusions about that data is considering potential errors. There are three main types of errors:”

• “Human error is a mistake by the experimenter. It is often described as an ‘experimenter’s error’ rather than experimental error. We don’t cite human error as a source of experimental error, unless there was an actual mistake made during the lab that affected the lab results. Examples include reading a scale wrong, spilling, or making a calculation mistake.”
• “Systematic error is an error inherent in the experimental set up, causing the results to be skewed in the same direction each time. For example, if you were determining acceleration due to gravity by measuring the fall time of a tennis ball, air resistance would produce a systematic error each time.”

Have students complete Part 4 of the Experimental Analysis Worksheet (S-7-6-3_Experimental Analysis Worksheet-Student Version.doc, and S-7-6-3_Experimental Analysis Worksheet-Teacher Version.doc).

Say, “Another common type of error is not an experimental one, but rather an error of communication. Look at the following graph:”

Say, “What does the graph suggest about Movie Theater A’s ticket sales in March compared to Movie Theater B’s ticket sales in the same month? It seems as though Movie Theater A made a lot more money than Theater B did. However, if you look at the y-axis scale, Movie Theater A only made 40 more dollars than Theater B did. The scale on the y-axis is scaled in very small increments, creating a biased graph.” Explain the concept of bias and how bias can affect scientific experiments.

Have students work on Part 5 of the Experimental Analysis Worksheet.

Extension:

• You can introduce students who may be going beyond the standards to quantitative error analysis such as percent error/percent difference. Percent error is used when you have a single experimental value that you wish to compare with a standard value.

Percent difference is useful when you have two experimental values obtained by different means that you wish to compare.