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Chapter 1

Biostats Chapter 1 Summary

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Department
Biology
Course
Biology 2244A/B
Professor
Jennifer Waugh
Semester
Fall

Description
Biostats 2244 Textbook Notes Chapter 1 – Introduction (Sampling and Study Design) 1-1 Overview Data – Observations (measurements, genders, survey responses) that have been collected Statistics – A collection of methods for planning experiments, obtaining data and then organizing, summarizing, analyzing, interpreting, presenting and drawing conclusions based on data Population – Complete collection of all elements (scores, people, measurements) to be studied. The collection is complete in that it includes all subjects to be studied Census – The collection of data from every member of the population Sample – A subcollection of members selected from part of a population Example: A poll asked 1000 adults a question. 1000 survey subjects constitute a sample, whereas the population would be all 202 million Americans. Every 10 years the government attempts a census of every citizen but that is almost impossible. Note: It is extremely important to obtain sample data that is representative of the population from which data are drawn. 1-2 Types of Data Parameter – A measurement describing some characteristic of a population. Example: If a freshwater pound is excavated and filled and stocked with 500 rainbow trout with a weight of 2100 pounds we get an average weight of 4.2 pounds. Since 500 is the total population the number 4.2 is a parameter. Statistic – A measurement describing some characteristic of a sample. Example: In a sample of 877 executives it is found that 45% would not hire someone with an error on their job application. 45% is a statistic since not every executive was surveyed. Quantitative Data – Consists of numbers representing counts or measurements (height, weight). It is important to use the appropriate units (dollars, hours, feet). Discrete Data – Results when the number of possible values is either a finite number or a countable number (the number of possible values is 0 or 1 or 2). Example: Chickens can lay 0, 1, 2, etc. eggs. They cannot lay an infinite amount of eggs. Continuous (numerical) Data – Result from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions or jumps. Example: The amount of milk from cows is continuous because they are measurements that can assume any value. A cow can yield an infinite amount of milk between 1 and 2 gallons (1.245 gallons) Note: Grammar dictates we use “fewer” for discrete amounts and “less” for continuous amounts Qualitative Data – Data can be separated into different categories that are non- numerical (eye color, gender). Nominal Level of Measurement – Characterized by data that consists of names, labels or categories only. The data cannot be arranged in an ordering scheme (low to high) Example: Survey responses of yes, no and undecided. Colors of pea pods (green, yellow) Ordinal Level of Measurement – Data that can be arranged in some order, but differences cannot be determined or are meaningless. Provide information about comparisons but not the magnitudes. Example: Course grades of A, B, C, D or F. There is an order but the differences cannot be calculated. Interval Level of Measurement – Similar to the ordinal level but with the additional property that the difference between any two data values is meaningful. Data at this level does not have a natural starting point. Example: Temperatures of 98.2 F and 98.6 F have a difference of 0.6 F but there o is no initial starting point – i.e. temperature does not start at 0 F. Ratio Level of Measurement – The interval level but with the additional property that there is a natural zero starting point. For values at this level but differences and ratios are both meaningful. Example: Weights of bald eagles. 0kg represents no weight and 4kg is twice as heavy as 2kg. o o Note: The difference between interval and ratio levels is difficult. 25 F is not half of 50 F but being age 4 is half of age 8. 1-3 Design of Experiments Successful use of statistics typically requires more common sense than mathematical expertise. Voluntary Response Sample – One in which the respondents themselves decide whether to be included. This method is flawed because if often happens that only people with strong interest or opinion will respond and thus responses are not representative of the whole population. If sample data are not collected in an appropriate way, the data may be so completely useless that no amount of statistical analysis can salvage them. Observational Study – We observe and measure specific characteristics but we don’t attempt to modify the subjects being studied Cross-Sectional Study – Data are observed, measured and collected at one point in time.
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