Upcoming SlideShare
×

# Biostatistics basics-biostatistics4734

408 views
310 views

Published on

Published in: Education, Technology
1 Comment
0 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Good Job.... Thanks... Would you like send me others material about it

Are you sure you want to  Yes  No
• Be the first to like this

Views
Total views
408
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
19
1
Likes
0
Embeds 0
No embeds

No notes for slide

### Biostatistics basics-biostatistics4734

1. 1. Biostatistics BasicsAn introduction to an expansive and complex field 1 © 2006
2. 2. Common statistical terms• Data – Measurements or observations of a variable• Variable – A characteristic that is observed or manipulated – Can take on different values E vid e nce -b as e d C h irop ractic 2 © 2006
3. 3. Statistical terms (cont.)• Independent variables – Precede dependent variables in time – Are often manipulated by the researcher – The treatment or intervention that is used in a study• Dependent variables – What is measured as an outcome in a study – Values depend on the independent variable E vid e nce -b as e d C h irop ractic 3 © 2006
4. 4. Statistical terms (cont.)• Parameters – Summary data from a population• Statistics – Summary data from a sample E vid e nce -b as e d C h irop ractic 4 © 2006
5. 5. Populations• A population is the group from which a sample is drawn – e.g., headache patients in a chiropractic office; automobile crash victims in an emergency room• In research, it is not practical to include all members of a population• Thus, a sample (a subset of a population) is taken E vid e nce -b as e d C h irop ractic 5 © 2006
6. 6. Random samples• Subjects are selected from a population so that each individual has an equal chance of being selected• Random samples are representative of the source population• Non-random samples are not representative – May be biased regarding age, severity of the condition, socioeconomic status etc. E vid e nce -b as e d C h irop ractic 6 © 2006
7. 7. Random samples (cont.)• Random samples are rarely utilized in health care research• Instead, patients are randomly assigned to treatment and control groups – Each person has an equal chance of being assigned to either of the groups• Random assignment is also known as randomization E vid e nce -b as e d C h irop ractic 7 © 2006
8. 8. Descriptive statistics (DSs)• A way to summarize data from a sample or a population• DSs illustrate the shape, central tendency, and variability of a set of data – The shape of data has to do with the frequencies of the values of observations E vid e nce -b as e d C h irop ractic 8 © 2006
9. 9. DSs (cont.) – Central tendency describes the location of the middle of the data – Variability is the extent values are spread above and below the middle values • a.k.a., Dispersion• DSs can be distinguished from inferential statistics – DSs are not capable of testing hypotheses E vid e nce -b as e d C h irop ractic 9 © 2006
10. 10. Hypothetical study data (partial from book) Case # Visits• Distribution provides a summary of: 1 7 2 2 – Frequencies of each of the values 3 2 • 2–3 4 3 • 3–4 5 4 • 4–3 etc. 6 3 7 5 • 5–1 8 3 • 6–1 9 4 • 7–2 10 6 11 2 – Ranges of values 12 3 • Lowest = 2 13 7 • Highest = 7 14 4 E vid e nce -b as e d C h irop ractic 10 © 2006
11. 11. Frequency distribution table Frequency Percent Cumulative % •2 3 21.4 21.4 •3 4 28.6 50.0 •4 3 21.4 71.4 •5 1 7.1 78.5 •6 1 7.1 85.6 •7 2 14.3 100.0E vid e nce -b as e d C h irop ractic 11 © 2006
12. 12. Frequency distributions are often depicted by a histogramE vid e nce -b as e d C h irop ractic 12 © 2006
13. 13. Histograms (cont.)• A histogram is a type of bar chart, but there are no spaces between the bars• Histograms are used to visually depict frequency distributions of continuous data• Bar charts are used to depict categorical information – e.g., Male–Female, Mild–Moderate–Severe, etc. E vid e nce -b as e d C h irop ractic 13 © 2006
14. 14. Measures of central tendency• Mean (a.k.a., average) – The most commonly used DS• To calculate the mean – Add all values of a series of numbers and then divided by the total number of elements E vid e nce -b as e d C h irop ractic 14 © 2006
15. 15. Formula to calculate the mean• Mean of a sample ΣX X = n• Mean of a population ΣX µ= N • X (X bar) refers to the mean of a sample and μ refers to the mean of a population • m is a command that adds all of the X values X • n is the total number of values in the series of a sample and N is the same for a population E vid e nce -b as e d C h irop ractic 15 © 2006
16. 16. Measures of central tendency (cont.)• Mode Mode – The most frequently occurring value in a series – The modal value is the highest bar in a histogramE vid e nce -b as e d C h irop ractic 16 © 2006
17. 17. Measures of central tendency (cont.)• Median – The value that divides a series of values in half when they are all listed in order – When there are an odd number of values • The median is the middle value – When there are an even number of values • Count from each end of the series toward the middle and then average the 2 middle values E vid e nce -b as e d C h irop ractic 17 © 2006
18. 18. Measures of central tendency (cont.)• Each of the three methods of measuring central tendency has certain advantages and disadvantages• Which method should be used? – It depends on the type of data that is being analyzed – e.g., categorical, continuous, and the level of measurement that is involved E vid e nce -b as e d C h irop ractic 18 © 2006
19. 19. Levels of measurement• There are 4 levels of measurement – Nominal, ordinal, interval, and ratio2. Nominal – Data are coded by a number, name, or letter that is assigned to a category or group – Examples • Gender (e.g., male, female) • Treatment preference (e.g., manipulation, mobilization, massage) E vid e nce -b as e d C h irop ractic 19 © 2006
20. 20. Levels of measurement (cont.)1. Ordinal – Is similar to nominal because the measurements involve categories – However, the categories are ordered by rank – Examples • Pain level (e.g., mild, moderate, severe) • Military rank (e.g., lieutenant, captain, major, colonel, general) E vid e nce -b as e d C h irop ractic 20 © 2006
21. 21. Levels of measurement (cont.)• Ordinal values only describe order, not quantity – Thus, severe pain is not the same as 2 times mild pain• The only mathematical operations allowed for nominal and ordinal data are counting of categories – e.g., 25 males and 30 females E vid e nce -b as e d C h irop ractic 21 © 2006
22. 22. Levels of measurement (cont.)1. Interval – Measurements are ordered (like ordinal data) – Have equal intervals – Does not have a true zero – Examples • The Fahrenheit scale, where 0° does not correspond to an absence of heat (no true zero) • In contrast to Kelvin, which does have a true zero E vid e nce -b as e d C h irop ractic 22 © 2006
23. 23. Levels of measurement (cont.)1. Ratio – Measurements have equal intervals – There is a true zero – Ratio is the most advanced level of measurement, which can handle most types of mathematical operations E vid e nce -b as e d C h irop ractic 23 © 2006
24. 24. Levels of measurement (cont.)• Ratio examples – Range of motion • No movement corresponds to zero degrees • The interval between 10 and 20 degrees is the same as between 40 and 50 degrees – Lifting capacity • A person who is unable to lift scores zero • A person who lifts 30 kg can lift twice as much as one who lifts 15 kg E vid e nce -b as e d C h irop ractic 24 © 2006
25. 25. Levels of measurement (cont.)• NOIR is a mnemonic to help remember the names and order of the levels of measurement – Nominal Ordinal Interval Ratio E vid e nce -b as e d C h irop ractic 25 © 2006
26. 26. Levels of measurement (cont.) Permissible mathematic Best measure of Measurement scale operations central tendency Nominal Counting Mode Greater or less than Ordinal Median operations Symmetrical – Mean Interval Addition and subtraction Skewed – Median Addition, subtraction, Symmetrical – Mean Ratio multiplication and division Skewed – MedianE vid e nce -b as e d C h irop ractic 26 © 2006
27. 27. The shape of data• Histograms of frequency distributions have shape• Distributions are often symmetrical with most scores falling in the middle and fewer toward the extremes• Most biological data are symmetrically distributed and form a normal curve (a.k.a, bell-shaped curve) E vid e nce -b as e d C h irop ractic 27 © 2006
28. 28. The shape of data (cont.) Line depicting the shape of the dataE vid e nce -b as e d C h irop ractic 28 © 2006
29. 29. The normal distribution• The area under a normal curve has a normal distribution (a.k.a., Gaussian distribution)• Properties of a normal distribution – It is symmetric about its mean – The highest point is at its mean – The height of the curve decreases as one moves away from the mean in either direction, approaching, but never reaching zero E vid e nce -b as e d C h irop ractic 29 © 2006
30. 30. The normal distribution (cont.) Mean The highest point of As one moves away from the overlying the mean in either direction normal curve is at the height of the curve the mean decreases, approaching, but never reaching zero A normal distribution is symmetric about its meanE vid e nce -b as e d C h irop ractic 30 © 2006
31. 31. The normal distribution (cont.) Mean = Median = ModeE vid e nce -b as e d C h irop ractic 31 © 2006
32. 32. Skewed distributions• The data are not distributed symmetrically in skewed distributions – Consequently, the mean, median, and mode are not equal and are in different positions – Scores are clustered at one end of the distribution – A small number of extreme values are located in the limits of the opposite end E vid e nce -b as e d C h irop ractic 32 © 2006
33. 33. Skewed distributions (cont.)• Skew is always toward the direction of the longer tail – Positive if skewed to the right – Negative if to the left The mean is shifted the most E vid e nce -b as e d C h irop ractic 33 © 2006
34. 34. Skewed distributions (cont.)• Because the mean is shifted so much, it is not the best estimate of the average score for skewed distributions• The median is a better estimate of the center of skewed distributions – It will be the central point of any distribution – 50% of the values are above and 50% below the median E vid e nce -b as e d C h irop ractic 34 © 2006
35. 35. More properties of normal curves• About 68.3% of the area under a normal curve is within one standard deviation (SD) of the mean• About 95.5% is within two SDs• About 99.7% is within three SDs E vid e nce -b as e d C h irop ractic 35 © 2006
36. 36. More properties of normal curves (cont.)E vid e nce -b as e d C h irop ractic 36 © 2006
37. 37. Standard deviation (SD)• SD is a measure of the variability of a set of data• The mean represents the average of a group of scores, with some of the scores being above the mean and some below – This range of scores is referred to as variability or spread• Variance (S2) is another measure of spread E vid e nce -b as e d C h irop ractic 37 © 2006
38. 38. SD (cont.)• In effect, SD is the average amount of spread in a distribution of scores• The next slide is a group of 10 patients whose mean age is 40 years – Some are older than 40 and some younger E vid e nce -b as e d C h irop ractic 38 © 2006
39. 39. SD (cont.) Ages are spread out along an X axis The amount ages are spread out is known as dispersion or spreadE vid e nce -b as e d C h irop ractic 39 © 2006
40. 40. Distances ages deviate above and below the mean Etc. Adding deviations always equals zeroE vid e nce -b as e d C h irop ractic 40 © 2006
41. 41. Calculating S2• To find the average, one would normally total the scores above and below the mean, add them together, and then divide by the number of values• However, the total always equals zero – Values must first be squared, which cancels the negative signs E vid e nce -b as e d C h irop ractic 41 © 2006
42. 42. Calculating S2 cont. S2 is not in the same units (age), but SD is Symbol for SD of a sample σ for a populationE vid e nce -b as e d C h irop ractic 42 © 2006
43. 43. Calculating SD with Excel Enter values in a columnE vid e nce -b as e d C h irop ractic 43 © 2006
44. 44. SD with Excel (cont.) Click Data Analysis on the Tools menuE vid e nce -b as e d C h irop ractic 44 © 2006
45. 45. SD with Excel (cont.) Select Descriptive Statistics and click OKE vid e nce -b as e d C h irop ractic 45 © 2006
46. 46. SD with Excel (cont.) Click Input Range iconE vid e nce -b as e d C h irop ractic 46 © 2006
47. 47. SD with Excel (cont.) Highlight all the values in the columnE vid e nce -b as e d C h irop ractic 47 © 2006
48. 48. SD with Excel (cont.) Click OK Check if labels are in the first row Check Summary StatisticsE vid e nce -b as e d C h irop ractic 48 © 2006
49. 49. SD with Excel (cont.) SD is calculated precisely Plus several other DSsE vid e nce -b as e d C h irop ractic 49 © 2006
50. 50. Wide spread results in higher SDs narrow spread in lower SDsE vid e nce -b as e d C h irop ractic 50 © 2006
51. 51. Spread is important when comparing 2 or more group meansIt is more difficult tosee a clear distinctionbetween groupsin the upper examplebecause the spread iswider, even though themeans are the sameE vid e nce -b as e d C h irop ractic 51 © 2006
52. 52. z-scores• The number of SDs that a specific score is above or below the mean in a distribution• Raw scores can be converted to z-scores by subtracting the mean from the raw score then dividing the difference by the SD X −µ z= σ E vid e nce -b as e d C h irop ractic 52 © 2006
53. 53. z-scores (cont.)• Standardization – The process of converting raw to z-scores – The resulting distribution of z-scores will always have a mean of zero, a SD of one, and an area under the curve equal to one• The proportion of scores that are higher or lower than a specific z-score can be determined by referring to a z-table E vid e nce -b as e d C h irop ractic 53 © 2006
54. 54. z-scores (cont.) Refer to a z-table to find proportion under the curveE vid e nce -b as e d C h irop ractic 54 © 2006
55. 55. Partial z-table (to z = 1.5) showing proportions of the z-scores (cont.)area under a normal curve for different values of z. Z 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.0 0.5000 0.5040 0.5080 0.5120 0.5160 0.5199 0.5239 0.5279 0.5319 0.5359 0.1 0.5398 0.5438 0.5478 0.5517 Corresponds to the 0.5714 0.5753 0.5557 0.5596 0.5636 0.5675 area 0.2 0.5793 0.5832 0.5871 0.5910 0.5948 0.5987 0.6026 0.6064 0.6103 0.6141 0.3 0.6179 0.6217 0.6255 0.6293 under the curve in black 0.6517 0.6331 0.6368 0.6406 0.6443 0.6480 0.4 0.6554 0.6591 0.6628 0.6664 0.6700 0.6736 0.6772 0.6808 0.6844 0.6879 0.5 0.6915 0.6950 0.6985 0.7019 0.7054 0.7088 0.7123 0.7157 0.7190 0.7224 0.6 0.7257 0.7291 0.7324 0.7357 0.7389 0.7422 0.7454 0.7486 0.7517 0.7549 0.7 0.7580 0.7611 0.7642 0.7673 0.7704 0.7734 0.7764 0.7794 0.7823 0.7852 0.8 0.7881 0.7910 0.7939 0.7967 0.7995 0.8023 0.8051 0.8078 0.8106 0.8133 0.9 0.8159 0.8186 0.8212 0.8238 0.8264 0.8289 0.8315 0.8340 0.8365 0.8389 1.0 0.8413 0.8438 0.8461 0.8485 0.8508 0.8531 0.8554 0.8577 0.8599 0.8621 1.1 0.8643 0.8665 0.8686 0.8708 0.8729 0.8749 0.8770 0.8790 0.8810 0.8830 1.2 0.8849 0.8869 0.8888 0.8907 0.8925 0.8944 0.8962 0.8980 0.8997 0.9015 1.3 0.9032 0.9049 0.9066 0.9082 0.9099 0.9115 0.9131 0.9147 0.9162 0.9177 1.4 0.9192 0.9207 0.9222 0.9236 0.9251 0.9265 0.9279 0.9292 0.9306 0.9319 1.5 0.9332 0.9345 0.9332 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9429 0.9441 E vid e nce -b as e d C h irop ractic 55 © 2006