Writing is Important! - PowerPoint PPT Presentation

writing is important n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Writing is Important! PowerPoint Presentation
Download Presentation
Writing is Important!

play fullscreen
1 / 64
Writing is Important!
305 Views
Download Presentation
victoria
Download Presentation

Writing is Important!

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Writing is Important! • Recommended reading: Clean, Well-lighted Sentences: A Guide to Avoiding the Most Common Errors in Grammar and Punctuation by Janis Bell

  2. Writing is Important! • Recommended reading: Eats Shoots and Leaves: The Zero Tolerance Approach to Punctuation by Lynne Truss

  3. Biology Lab Background III / Properties of Light September 15, 2008 Overview of Microscopy Dr. Behonick

  4. Topics for today … • Biology Lab Background III • Experimental Design • Types of Data • Properties of Light

  5. Experimental design

  6. The Scientific Method Make Observation(s) from http://asweknowit.net/MIDDLE_SCH/DWA_7_scientific_method.htm

  7. Types of experiments • in vitro • “within the glass” • performed outside a living organism in a controlled environment (ex = in a test tube) • in vivo • “within the living” • performed in/on living tissue of intact organism • ex vivo • “out of the living” • performed in/on living tissue in artificial environment outside organism from which it was harvested (ex = cell culture) • in silico • performed entirely on computer or by computer simulation

  8. Variables • variable = what is measured or manipulated in an experiment • independent variable = variable you have control over, what you can choose and manipulate (value(s) you are manipulating, also known as “manipulated variable”) • dependent variable = what you measure in experiment (what is affected during experiment, responds to independent variable)

  9. Examples • effect of different doses of a drug (IV) on severity of disease symptoms (DV) • effect of different quantities of fertilizer (IV) on how your house plants grow (DV) • would want to control other variables like water, soil, size of pot, time in sun, etc. • effect of different water temperatures (IV) on how fast a sugar cube will dissolve (DV) • effect of paper towel brand (IV) on how much water can be soaked up with one paper towel sheet

  10. Cause & Effect change in dependent variable (effect) manipulation of independent variable (cause)

  11. Controlling for Bias • blindedness - controlling for conscious/unconscious bias in research • placebo effect = subject receiving placebo reports change in symptoms (despite lack of actual chemical treatment) due to expectation or belief that it will work • observer bias = error in observation/ measurement when observers overemphasize behaviors they expect to find & fail to notice behavior they don’t expect

  12. Controlling for Bias • single-blind study = subjects are blinded but experimenters are not • ex = subject does not know whether receiving drug or sugar pill • experimentor either can’t be blinded due to design of study or doesn’t need to be because can’t introduce further bias • double-blind study = both subjects and experimenters are blinded • subjects randomly assigned to groups, experimenters don’t know assignments • master list of group assignments kept by third party until experiment finished • “triple-blind” study = double-blind study in which person interpreting results is also blinded (ex = statistician)

  13. What is enough proof? • statistical analyses • looking for patterns in your data after accounting for randomness/uncertainty and using this information to draw inferences about process/population being studied • are these results a big enough deal for me to care? • are these results due to random chance? • are these results generalizable?

  14. What is enough proof? • sample size = # of observations (or pieces of collected data) that constitute a result • ex = # of subjects per group in experiment • if you’re trying to make a general statement about a population, bigger sample size  more precise statement

  15. Controls! • internal proof for your experiment • negative control - shows that a negative result is possible in your system • positive control - shows that a positive result is possible in your system • “What else could have caused observed effect?”

  16. Baking bread: a tale of good controls

  17. Baking bread: a tale of good controls • does yeast Dani found in the back of the freezer still work? • experimental design • controls

  18. Baking bread: a tale of good controls negative control positive control shows a negative result is possible shows a positive result is possible

  19. Baking bread: a tale of good controls negative result positive result

  20. Types of Data

  21. NOTE • datum = singular • a single measurement, result, etc. • data = plural • a collection of measurements, results, etc.

  22. Types of Data • quantitative data - numerical data • scale of measurement has magnitude (some things are bigger than others) • ex - height, cholesterol level • qualitative data - not numerical data, may be categorical or descriptive • scale of measurement is a set of unordered categories • ex - types of trees, types of compounds

  23. Quantitative Data • described in terms of numerical quantity • discrete data - there are only a finite # of values possible & values can’t be subdivided and still be meaningful (ex - population data) • continuous data - data that can be measured on a continuum (physical measurements are generally this type of data); can have almost any # value and be subdivided and still be meaningful • can be displayed in charts, tables, graphs, histograms • can be analyzed using statistics

  24. Qualitative Data • described on basis of relative characteristics • color • shape • texture • temperature • odor • taste (generally not used in research science) • sometimes considered “less valuable” by research scientists

  25. Example 1 Qualitative data - pirates carry parrots while ninjas do not.

  26. Example 1 Quantitative data - there are 6 pirates & 2 ninjas.

  27. Example 2 • hot fudge sundae • qualitative data • cold to touch • creamy texture • serving glass is colorless & transparent • quantitative data • serving temperature is -10oC • serving glass is 6 inches in height • cost $6.95

  28. Microscopy Data • microscopists collect both qualitative & quantitative data • qualitative data • color of specimen • overall structure of specimen • shape of cells • type of cells present & their location • quantitative data • how much bigger is one specimen (or one particilar region of a specimen) vs. another? • how many cells are in one part of a specimen vs. another?

  29. from Behonick, et al. (2007) PLoS One

  30. Data Collection • accuracy = closeness of measured value to a standard value. accuracy is independent of precision. • ex - if in lab you obtain a weight measurement of 3.2 kg for a given substance, but known weight is 10 kg, then your measurement is not accurate (not close to known value) • precision = the closeness of 2 or more measurements to each other. precision is independent of accuracy. • ex = if you weigh 10 kg substance 5 times & get 3.2 kg each time, then your measurement is very precise but not accurate.

  31. Accuracy vs. Precision

  32. Properties of Light

  33. Light • travels @ 186,000 miles/sec • ≈ 669,600,000 miles/hour • can think of light as • stream of tiny particles/energy packets (photons) • a wave (light waves) • we’ll stick with this interpretation

  34. The thing about waves … • they’re made up of energy, not matter • at the beach • at the laundromat

  35. <cue simple harmonic wave animation>

  36. Measuring Waves • period (T) = time to one complete wave cycle • frequency () = # periods per unit time; measured in Hz • ex = # waves that pass a particular point in space during specific time interval • wavelength (l) = distance between same point on 2 sequential waves (ie - 2 sequential peaks, 2 sequential troughs) • amplitude (A) = maximum distance from highest point of peak to equilibrium in 1 wave cycle A amount of time required to complete = T

  37. Frequency

  38. The thing about waves … • they’re made up of energy, not matter • at the beach • at the laundromat • light waves ~ water waves but don’t need medium to travel thru • can move thru medium or vacuum • fastest in vacuum, slow down in medium • energy in light waves = electrical & magnetic fields •  light = electromagnetic radiation

  39. 1 m 106 nm 106 nm 10–5 nm 1 nm 10–3 nm 103 nm 103 m Micro- waves Radio waves Gamma rays X-rays UV Infrared Visible light 380 450 500 550 600 650 700 750 nm Shorter wavelength Longer wavelength Lower energy Higher energy EM Spectrum wavelengths 400 – 700 nm constitute visible light for humans higher frequency lower frequency

  40. EM Resources • EM Wave Propagation Tutorial http://micro.magnet.fsu.edu/primer/java/electromagnetic/index.html • Basic EM Wave Properties Tutorial http://micro.magnet.fsu.edu/primer/java/wavebasics/index.html

  41. transmitted reflected Properties of Light absorbed

  42. Microscopy Techniques objective light source transmitted bright field phase DIC

  43. objective light source Transmitted Light • amplitude object = pigmented or stained sample • ex = histology specimens • seen w/ brightfield microscopy • phase object • ex = most biological samples • seen w/ phase or DIC microscopy

  44. Objects and transmitted light light wave amplitude object seen as color phase object not seen

  45. Contrast • contrast = difference in color & light between parts of an object/image • requred to see an object by microscope • can come from variations in • intensity (DIC, phase) • color (bright field, fluorescence)

  46. Contrast • cells typically are • transparent (not amplitude objects) • phase objects • low in contrast • contrast-generating techniques turn phase differences into intensity differences so we can see unstained cells using transmitted light • ex = DIC microscopy

  47. Refraction • refraction = bending of light when it passes from a medium of one density into a medium of another density • light travels at different speeds through different media (ex = air, water, glass)

  48. Refraction

  49. Properties of Light refracted