INQUIRY: Kinetic Theory of Matter

INQUIRY: Kinetic Theory of Matter

Biologist ______Date ______

INQUIRY: Kinetic Theory of Matter

By: Mark Welch

Kinetic theory of matter states that all matter is made of tiny particles that move. The more energy these particles have the further they will be apart and the more vigorous their activity will be. Temperature is the measure of the average kinetic energy of these particles. Water exhibits adhesion, which means that it sticks to other surfaces. It does this due to its polarity. The charged ends of the water bond with charges on the surface of any object. Water also exhibits cohesion, which means that water molecules will stick to each other by the same polarity concept. At the boundary of air and water this will form a layer that is known as surface tension. Some insects and small animals can literally walk on water due to its surface tension. Using this knowledge form a hypothesis for the result of this lab.

Each lab station (A, B, and C) has water that is different temperature. A will be the hottest, followed by B and then C. Using the 1.0 mL pipette, place as many droplets on the penny as you can until its surface tension bubble breaks releasing the water off the top of the penny. Count the drops as you go. Record the number of drops in the table below. Repeat with the other lab partner. When complete move to the other two stations completing the same procedure.

HYPOTHESIS:

Trial / Beaker A (drops) / Beaker B (drops) / Beaker C (drops) / Student
1
2
3
4
Average

CONCLUSION:

Kinetic Theory of Matter Inquiry- Part 2

Using a statistical test to analyze data is the true meaning of data analysis. Thus far in your research history you may have "analyzed" data using total counts or averages, but these DO NOT indicate how the experiment was designed therefore producing numbers that ARE NOT scientifically sound. A statistical test does these things. A statistical test compares the numbers that make up the average to quantitate uncontrolled variables within the experimental design. From this analysis the researcher can address what may have been something that affected the outcome that was not being used as a variable. These things are called uncontrolled variables. We will learn to use a data analysis tool called a t-Test to analyze our data. It is found in Microsoft Excel therefore data must be entered in that program to be able to analyze it. This will be done for you by your instructor. You will then learn how to use the tool by answering the following questions below.

  1. What is the mean (average) for A?
  2. What is the mean (average) for B?
  3. What is the mean (average) for C?
  4. What is the hypothesized mean difference?
  5. What is the alpha?
  6. Which p value should you use (If you stated that one group would perform better than the others in your hypothesis you select a one-tailed comparison, if you stated that merely a difference would exist then you will select a two-tailed comparison?
  7. When comparing A and B, was there a significant difference using the correct p value?

Was it less than 0.01?Was it less than 0.001?

  1. When comparing A and C, was there a significant difference using the correct p value?

Was it less than 0.01?Was it less than 0.001?

  1. When comparing B and C, was there a significant difference using the correct p value?

Was it less than 0.01?Was it less than 0.001?

Variance is the measure of variables that were uncontrolled in the experimental design. High variances produce unreliable numbers that make an average. When variances are high p values are normally more than the alpha meaning that significant differences in the numbers that make an average are not reliable enough due to uncontrolled variables.

  1. What was the variance of A?B?C?
  2. How did this affect the result of the experiment?
  3. What were possible uncontrolled variables?

Significant differences indicate statistically different numbers. A significant difference is when the p value is less than, or equal to, the alpha.

  1. Is A significantly different than B?
  2. Is A significantly different than C?
  3. Is B significantly different than C?

If significant differences exist, the researcher will reject the null hypothesis (statistical hypothesis meaning p value is more than the alpha) and accept an alternative hypothesis (statistical hypothesis meaning the p value is less than the alpha). If a significant difference does not exist, the researcher will accept the null hypothesis.

  1. Which hypothesis results from A compared to B?
  2. Which hypothesis results from A compared to C?
  3. Which hypothesis results from B compared to C?

A confidence interval is the number of times the experiment could be replicated with a similar result. This is a powerful aspect of the data analysis. It is calculated by subtracting the p value from 1.00 and multiplying by 100%. A confidence interval is not calculated for a null hypothesis.

  1. What is the confidence interval for A compared to B?
  2. What is the confidence interval for A compared to C?
  3. What is the confidence interval for B compared to C?

Standard error is a powerful way to show if averages are significantly different by the addition of error bars to existing graphs. Standard error allows for numerical representation of uncontrolled variables by a simple calculation: Standard Error is equal to the square root of the result of dividing the variance by the number of observations. The result is the amount plus or minus the average. It indicates how much more or less the average may be due to uncontrolled variables.

  1. What is the standard error for A?
  2. What is the standard error for B?
  3. What is the standard error for C?
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