BIO 305: Exam 1 Review
BIO 305: Exam 1 Review BIO 305
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This 5 page Study Guide was uploaded by Emily.nicole on Thursday December 3, 2015. The Study Guide belongs to BIO 305 at Syracuse University taught by Dr. Mark Ritchie, Dr. Kate Lewis, & Dr. Eleanor Maine in Spring 2014. Since its upload, it has received 187 views. For similar materials see Integrative Biology Lab in Biology at Syracuse University.
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Date Created: 12/03/15
POPULATION VS. SAMPLE A POPULATIONIS ACOLLECTION OF DATAWHOSE PROPERTIES ARE ANALYZED. THE POPULATION ISTHE COMPLETE COLLECTIONTO BE STUDIED, IT CONTAINALL SUBJECTS OF INTEREST. A SAMPLEIS PARTOF THE POPULATION OF INTEREST, SUB-COLLECTIONSELECTED FROM A POPULATION. THE NORMAL DISTRIBUTION Example: Changing μshifts the distribution left or right. Changing σ increases or decreases the spread. Normal distribution is defined by its mean and standard dev. MEAN μ: mean=AVERAGE Excel Syntax:Mean: AVERAGE(starting cell: ending cell) Median = middle value Excel : MEDIAN(starting cell: ending cell) Mode= most often Excel : MODE(starting cell: ending cell) Standard Deviation- quantifies variability σ: standard deviation -GIVES AN IDEA OF HOW CLOSE THE ENTIRE SET OF DATA IS TO THE AVERAGE VALUE STDEV(starting cell: ending cell) Standard Error of the Mean -quantifies the percision of the mean STDEV(starting cell: ending cell)/SQRT(number) SEx = s/ sq rt. of n s= standard deviationn= sample size SD VS. SEM THE SDQUANTIFIES VARIBILITY — HOW MUCH THE VALUES VARY FROM ONE ANOTHER. THE SEMQUANTIFIES HOW PRECISELY YOU KNOW THE TRUE MEAN OF THE POPULATION. IT TAKES INTO ACCOUNT BOTH THE VALUE OF THE SD AND THE SAMPLE SIZE. THE EM, BY DEFINITION, IS ALWAYS SMALLER THAN TSD. THE SEMGETS SMALLER AS YOUR SAMPLES GET LARGER. THE DDOES NOT CHANGE PREDICTABLY AS YOU ACQUIRE MORE DATA. What’s hypothesis is a proposeexplanatiofor aphenomenon. testable Most of the time a hypothesis is written like this: "If _____[I do this] _____, then _____[this]_____ will happen." Logic : Assume A. If A, then B. Not B. Therefore, Not A. Steps of hypothesis testing 1. Initial observation 2. Define your hypothesis (null H0, alternative/experimental Ha) 3. Do experiments to test your hypothesis (including Information gathering) 4. Record observation 5. Do statistics (calculate the p-value of what you observed) 6. Conclusion (accept/ reject your hypothesis) Types of T. TEST independent t.testindependent samples -- compares means between two independent groups. Paired t.tes: paired (dependent) samples -- compares means between two related groups . e.g.: 1) measurements were taken from the same group twice (repeated measures) 2) measurements are joined, for example, comparing the IQ of older and younger brothers The T-Test The ttest assesses whether the means of two groups are statisticall different from each other. ANOVA=ANalysis Of VAriance compares means between more than two independent groups (there’s extension) Just an extension of the t-test One-way ANOVA (single factor): an ANOVA with only two groups, is mathematically equivalent to a t-test Two-way ANOVA (multiple factors) Chi-square test- goodness of fit compares proportions between two or more groups- frequencies only categorical variable Example: Limiting Factor: Limiting means the factthat effects the growth or development of an organism, population, or process
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