1.1 STATISTICS

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1.1 STATISTICS

  1. 1. STATISTICS!!! The science of data
  2. 2. What is data? <ul><li>Information, in the form of facts or figures obtained from experiments or surveys, used as a basis for making calculations or drawing conclusions </li></ul><ul><li>Encarta dictionary </li></ul>
  3. 3. Statistics in Science <ul><li>Data can be collected about a population (surveys) </li></ul><ul><li>Data can be collected about a process (experimentation) </li></ul>
  4. 4. 2 types of Data <ul><ul><li>Qualitative </li></ul></ul><ul><ul><li>Quantitative </li></ul></ul>
  5. 5. Qualitative Data <ul><li>Information that relates to characteristics or description (observable qualities) </li></ul><ul><li>Information is often grouped by descriptive category </li></ul><ul><li>Examples </li></ul><ul><ul><li>Species of plant </li></ul></ul><ul><ul><li>Type of insect </li></ul></ul><ul><ul><li>Shades of color </li></ul></ul><ul><ul><li>Rank of flavor in taste testing </li></ul></ul><ul><ul><li>Remember: qualitative data can be “ scored ” and evaluated numerically </li></ul></ul>
  6. 6. Qualitative data, manipulated numerically <ul><li>Survey results, teens and need for environmental action </li></ul>
  7. 7. Quantitative data <ul><li>Quantitative – measured using a naturally occurring numerical scale </li></ul><ul><li>Examples </li></ul><ul><ul><li>Chemical concentration </li></ul></ul><ul><ul><li>Temperature </li></ul></ul><ul><ul><li>Length </li></ul></ul><ul><ul><li>Weight…etc. </li></ul></ul>
  8. 8. Quantitation <ul><li>Measurements are often displayed graphically </li></ul>
  9. 9. Quantitation = Measurement <ul><li>In data collection for Biology, data must be measured carefully, using laboratory equipment </li></ul><ul><li>( ex. Timers, metersticks, pH meters, balances , pipettes, etc) </li></ul><ul><li>The limits of the equipment used add some uncertainty to the data collected. All equipment has a certain magnitude of uncertainty. For example, is a ruler that is mass-produced a good measure of 1 cm? 1mm? 0.1mm? </li></ul><ul><li>For quantitative testing, you must indicate the level of uncertainty of the tool that you are using for measurement!! </li></ul>
  10. 10. How to determine uncertainty? <ul><li>Usually the instrument manufacturer will indicate this – read what is provided by the manufacturer. </li></ul><ul><li>Be sure that the number of significant digits in the data table/graph reflects the precision of the instrument used (for ex. If the manufacturer states that the accuracy of a balance is to 0.1g – and your average mass is 2.06g, be sure to round the average to 2.1g) Your data must be consistent with your measurement tool regarding significant figures . </li></ul>
  11. 11. Finding the limits <ul><li>As a “ rule-of-thumb ” , if not specified, use +/- 1/2 of the smallest measurement unit (ex metric ruler is lined to 1mm,so the limit of uncertainty of the ruler is +/- 0.5 mm.) </li></ul><ul><li>If the room temperature is read as 25 degrees C, with a thermometer that is scored at 1 degree intervals – what is the range of possible temperatures for the room? </li></ul><ul><li>(ans.s +/- 0.5 degrees Celsius - if you read 15 o C, it may in fact be 14.5 or 15.5 degrees) </li></ul>
  12. 12. Looking at Data <ul><li>How accurate is the data? (How close are the data to the “ real ” results?) This is also considered as BIAS </li></ul><ul><li>How precise is the data? (All test systems have some uncertainty, due to limits of measurement) Estimation of the limits of the experimental uncertainty is essential. </li></ul>
  13. 15. Comparing Averages <ul><li>Once the 2 averages are calculated for each set of data, the average values can be plotted together on a graph, to visualize the relationship between the 2 </li></ul>
  14. 18. Drawing error bars <ul><li>The simplest way to draw an error bar is to use the mean as the central point, and to use the distance of the measurement that is furthest from the average as the endpoints of the data bar </li></ul>
  15. 19. Average value Value farthest from average Calculated distance
  16. 20. What do error bars suggest? <ul><li>If the bars show extensive overlap, it is likely that there is not a significant difference between those values </li></ul>
  17. 22. Quick Review – 3 measures of “ Central Tendency ” <ul><li>mode : value that appears most frequently </li></ul><ul><li>median : When all data are listed from least to greatest, the value at which half of the observations are greater, and half are lesser. </li></ul><ul><li>The most commonly used measure of central tendency is the mean , or arithmetic average (sum of data points divided by the number of points)      </li></ul>
  18. 23. How can leaf lengths be displayed graphically?
  19. 24. Simply measure the lengths of each and plot how many are of each length
  20. 25. If smoothed, the histogram data assumes this shape
  21. 26. This Shape? <ul><li>Is a classic bell-shaped curve, AKA Gaussian Distribution Curve, AKA a Normal Distribution curve. </li></ul><ul><li>Essentially it means that in all studies with an adequate number of datapoints (>30) a significant number of results tend to be near the mean. Fewer results are found farther from the mean </li></ul>
  22. 27. <ul><li>The standard deviation is a statistic that tells you how tightly all the various examples are clustered around the mean in a set of data </li></ul>
  23. 28. Standard deviation <ul><li>The STANDARD DEVIATION is a more sophisticated indicator of the precision of a set of a given number of measurements </li></ul><ul><ul><li>The standard deviation is like an average deviation of measurement values from the mean. In large studies, the standard deviation is used to draw error bars, instead of the maximum deviation. </li></ul></ul>
  24. 29. A typical standard distribution curve
  25. 30. According to this curve: <ul><li>One standard deviation away from the mean in either direction on the horizontal axis (the red area on the preceding graph) accounts for somewhere around 68 percent of the data in this group. </li></ul><ul><li>Two standard deviations away from the mean ( the red and green areas ) account for roughly 95 percent of the data. </li></ul>
  26. 31. Three Standard Deviations? <ul><li>three standard deviations (the red, green and blue areas) account for about 99 percent of the data </li></ul>-3sd -2sd +/-1sd 2sd +3sd
  27. 32. How is Standard Deviation calculated? <ul><li>With this formula! </li></ul>
  28. 33. AGHHH! Ms. Pati <ul><li>DO I NEED TO KNOW THIS FOR THE TEST????? </li></ul>
  29. 34. Not the formula! <ul><li>This can be calculated on a scientific calculator </li></ul><ul><li>OR…. In Microsoft Excel, type the following code into the cell where you want the Standard Deviation result, using the &quot;unbiased,&quot; or &quot;n-1&quot; method: =STDEV(A1:A30) (substitute the cell name of the first value in your dataset for A1, and the cell name of the last value for A30.) </li></ul><ul><li>OR….Try this! http://www.pages.drexel.edu/~jdf37/mean.htm </li></ul>
  30. 35. You DO need to know the concept! <ul><li>standard deviation is a statistic that tells how tightly all the various datapoints are clustered around the mean in a set of data. </li></ul><ul><li>When the datapoints are tightly bunched together and the bell-shaped curve is steep, the standard deviation is small.(precise results, smaller sd) </li></ul><ul><li>When the datapoints are spread apart and the bell curve is relatively flat, a large standard deviation value suggests less precise results </li></ul>
  31. 36. THE END <ul><li> </li></ul><ul><li>For today ………. </li></ul>

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