Introduction to Science

1. Introduction to Science • Conceptual Physics

2. Nature of Science • Science • views the universe as regular and predictable, not random and chaotic • is about discovering explanations on how the universe works based on verifiable and testable evidence • Evidence should be coupled with reason, logic, and skepticism.

3. A Search for Patterns • The human mind seeks order. • Our first explanations of nature were based on spiritual beliefs. • Later, the ancient Greeks, Chinese, & Persians searched for patterns in nature. • Consistency in patterns must be governed by basic principals. • Understanding these principals gives us the power ofprediction.

4. Observation and Technology • Science helps us understand how the universe works. • In the pursuit of “how” questions, science leads to the production of technology. • Technological advances help us make new observations of the universe. • Science and technology leapfrog each other, each making advances in the other possible.

5. The Basic Science • The study of science today branches into the study of living and non-living things. • Life Sciences • Biology, Zoology, Botany • Physical Sciences • Geology, Astronomy, Chemistry • Physics • Physics is the most basic science • Motion, Forces, Energy, Matter, Heat, Sound, Light, and the composition of atoms • All other sciences rely on an understanding of Physics.

6. Scientific Methods 6

7. The Scientific Method

8. Levels of Confidence • Experiments repeated with the same results by multiple people over time lead to hypotheses with a high degree of confidence. • Hypotheses with a high degree of confidence can be elevated to the status of theory, or a statement with extremely high confidence. • A scientificlawstates a repeated observation about nature. • Models (or analogues) are used in science to represent things in nature that are too big, small, or complex to study easily. • Mathematical models (equations) are used to make predictions about natural phenomena.

9. Science Has Limits • The phenomenon must betestable. • No knowledge is absolute. • What we believe to be true today may be obsolete tomorrow. • Science does not prove anything, only gives evidence to support a claim. • Science can’t answer all questions. • Science answers “how” questions. • How did the universe begin? • “Why” questions are out of the bounds of science and should be left to philosophers. • Why is there a universe?

10. Many theories and laws can be described using mathematics. Qualitativestatements describe observations seen in the universe. Quantitativestatements are mathematical equations that describe scientific theories and laws. Qualitative vs. Quantitative

11. Units of Measurement • Mathematics is the language of science. • Measurements give scientists the values used in the formulas that describe nature quantitatively. • All scientists need a consistent system of measurement in order to communicate their ideas and discoveries. • The International System of Units(SI) is used throughout the world.

12. SI Base Units in Physics • Length- METER • Mass- KILOGRAM • Time- SECOND • Temperature- KELVIN • Electric Current- AMPERE • Amount of Substance- MOLE • Luminous Intensity- CANDELA

13. SI Units • Combinations of base units are called derived units. • Area (l • w) {units ex.: m • m = m2} • Volume (l • w • h) {units ex.: m • m • m = m3} • Pressure (F ⁄ A) {units ex.: N ⁄ m2} • Pressure unit is called the Pascal • Force (m • a) {units ex.: kg • m ⁄ s2} • Force unit is called the Newton • Weight is an example of force • Speed (d ⁄ t) {units ex.: m ⁄ s}

14. Metric System- multiples of ten Now that’s just silly!

15. Prefixes Used For Large Measurements • KILO- thousand • Times 1,000 or 103 • MEGA- million • Times 1,000,000 or 106 • GIGA- billion • Times 1,000,000,000 or 109

16. Prefixes Used for Small Measurements • DECI- tenth • Times 0.1 or 10-1 • CENTI- Hundredth • Times 0.01 or 10-2 • MILLI- thousandth • Times 0.001 or 10-3 • MICRO- millionth • Times 0.000 001 or 10-6 • NANO- billionth • Times 0.000 000 001 or 10-9

17. http://physics.nist.gov/cuu/Units/prefixes.html

18. Used to write very large and very small numbers pattern: a x 10b a is thecoefficient, (AKA the mantissa) b is theexponent The mantissa must be 1 or greater and less than 10 (digits 1–9). Theexponentis determined by how many places the decimal must be moved when converting into scientific notation. The Earth’s mass is about 5,973,600,000,000,000,000,000,000 kg In scientific notation it is written 5.9736 x 1024 kg Scientific Notation

19. 10000 0.001 4081973.701 4 600 000 000 000 Scientific Notation Practice • 1 x 104 • 1 x 10-3 • 4.081973701 x 106 • 4.6 x 1012 • 100 • 0.01 • 150 000 • 3.0000 x 102 • 102 • 10-2 • 1.5 x 105 • 300.00

20. Conversion factor- a fraction that is mathematically equivalent to 1 that relates two different units (both must be same kind of unit (e.g. length, mass, etc.)) Numerator and denominator represent the same thing in two different units E.g. 12 in ⁄ 1 ft; 1 m ⁄ 3.3 ft Converting to a larger unit results in a smaller number (e.g. 234 m = 0.234 km) RULES: 1. Start with what you are given and put it over 1 2. Multiply by the conversion factor to cancel out the units you begin with and work toward the units you want Unit Conversion

21. Unit Conversion Practice • Convert 4.08 meters to centimeters • 408 cm = 4.08 x 102 cm • Convert 13 milliseconds to seconds • 13 ms = 13 x 10–3 s = 1.3 x 10–2 s • Convert 15 megaamperes to amperes • 15 MA = 15 x 106 A = 1.5 x 107 A • Convert 875 gigagrams to nanograms • 875 x 109 g = 8.75 x 1011 g = 8.75 x 1020 ng

22. Organizing Data • Line Graphs • Best for displaying data that change • Easy to see trends • Two variables • Independent • X-axis • Dependent • Y-axis • Title, axes labeled, units included • Reader should be able to understand what took place in the experiment by looking at the graph

23. Organizing Data • Bar Graphs • Best for comparing data • Makes differences in values more clear to the reader • Title, axes labeled, units included

24. Organizing Data • Pie charts • Best for displaying data that are parts of a whole • Percentages • Title, legend, data

25. Graphing Terminology • Line of Best Fit (LOBF) • Shows thetrend of the plotted points • Draw the line over as many points as possible with the same number of points balanced above and below the ruler • Extrapolate • Constructing new data points outside the data set plotted • Extend the line of best fit • Linear vs. non-linear • Straight line vs. curved line • Slope • Pick two points on the LOBF far from each other. • Calculate rise ⁄ run (difference in y values divided by difference in x values). y2-y1 / x2-x1

26. Experimentation and Graphing • Constant- only ONE variable is changed • Variable- • Independent- the variable that is manipulated (systematically changed) • X-axis • Dependent- any change that results from manipulating the independent variable • Y-axis • DRY MIX • Dependent variable depends on the independent variable • Control- a sample that is treated exactly like the experimental group except that the independent variable is not manipulated

27. Direct and Inverse Relationships • In math or statistics, a direct relationshipis a relationship between two variables in which they both increase or decrease in conjunction • In this example, an increase in x results in an increase in y

28. Direct and Inverse Relationships • In math or statistics, an inverse relationship is a relationship between variables in which one variable decreases as the other increases • In this example, an increase in x results in a decrease in y

29. Accuracy vs. Precision • Accuracy- how close a measured value is to its accepted, or true, value • Precision- how close a series of measurements are to one another • Bull’s eye of the target will be considered the true value

30. Examples • Good accuracyGood precision • Poor accuracyGood precision • Good accuracyPoor precision • Poor accuracyPoor precision

31. Accuracy vs. Precision true value accurate but not precise precise but not accurate not precise, not accurate precise and accurate

32. Three Sources of Error • Random Error • Fluctuation in measurements about some value–sometimes larger, sometimes smaller • Cancels out, on average, with many values • Systematic Error • Due to mis-calibrated instrument or one with a zero error • Reading Error • Error in measurement inherent to measuring instrument (e.g. ± 1 mm with a meterstick)

33. Review question:The following measurements were made for the density of lead. Each student measured their piece of lead three times:

34. Rachel: 11.32 g/ml, 11.35 g/ml, 11.33 g/mlDaniel: 11.43 g/ml, 11.44 g/ml, 11.42 g/mlRobert: 11.55 g/ml, 11.34 g/ml, 11.04 g/mlThe actual density of lead is 11.34 g/ml.Which person’s measurements were the most accurate? Which were precise? Which were both accurate and precise?