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BCOR 102 - Exam 1
exp. and log. growth, statistics, definitions
| Question | Answer |
|---|---|
| Deduction | drawing conclusions from the general to the specific |
| Induction | drawing conclusions from the specific to the general |
| Inductive method | observation->hypothesis->predictions -> new observations. Predictions may or may not be confirmed - if it is, hypothesis is confirmed. If not, must modify hypothesis to account for original observation. Spiral upwards, ignorance to knowledge |
| Paradigms | Kuhn - a view of nature that implicitly defines legitimate problems and methods for science. Paradigm->puzzle solving-> anomolies (resolved, theory expanded, or remain isolated)->crisis->scientific revolution->new paradigm |
| Hypothetico Deductive Method | Popper (opposed Kuhn) - observation-> hyp. A, B, and C. Predictions A, B, and C. Test...etc. Want to get a negative result so that you can discard some hypotheses. Left w/ a hyp that cannot be disproved |
| Compare IM and HDM: hypotheses | ID: confirm hyp HDM: refute hyp |
| Compare IM HDM: number of hyp | ID: single hyp is repeatedly modified HDM: multiple hyps are tested and some discarded |
| Compare IM and HDM: progress | IM: pregress through accumulation of knowledge HDM: progress through destruction |
| Problems w/ IM | 1. alternative hypotheses aren't really considered. 2. more than one might actually be true. 3. no guarantee that IM leads to the correct answer. |
| Problems w/ HDM | 1. list of hyps must include the "correct" one. 2. hyps may not be mutually exclusive - more than one mechanism may be occurring. 3. when do we discard a hyp? |
| Type II statistical error | "false negative": the error of failing to reject a null hypothesis when it is in fact not true. Error of failing to observe a difference when in truth there is one. An example would be if a test shows that a woman is not pregnant when in reality she is. |
| Type I statistical error | "false positive": the error of rejecting a null hypothesis when it is actually true. Occurs when we are observing a difference when in truth there is none. Example would be if a test shows that a woman is pregnant when in reality she is not. |
| environmental stochasticity | uncertainty due to variation in environmental conditions. In exp. growth model, it is expressed in the mean and variance in r. |
| Population | a group of individuals, all of the same species, live in the same place, and have the potential to reproduce. |
| Type I survivorship curve | survival probabilities are relatively high for young individuals and relatively low for old individuals (mammals) - invest in parental care |
| Type II survivorship curve | survival probabilities are relatively constant across different ages - few species show this |
| Type III survivorship curce | survival probabilities are low for young individuals and high for old individuals - plants, invertebrates - large numbers of offspring |
| Iteroparous | organisms that reproduce at more than one age in their life history |
| semelparous | organisms where reproduction is concentrated at a single age |
| stationary age distribution | distribution where both absolute and relative numbers of individuals represented in each age remain constant. Special case of stable age distribution where r equals zero. |
| stable age distribution | individuals represented in each age of an exponentially increasing or decreasing population. |
| Tragedy of the Commons | everyone benefits until someone takes advantage of the situation - impossible to fight against because everyone is concerned more for their own short term gains instead of long term maximum yields. |
| Demographic stochasticity | fluctuations in population size due to random birth and death sequences |
| net reproductive rate (Rsub 0) | the mean number of female offspring produced by a female over her lifetime. Gross number of offspring minus the chances of female survivorship through different ages R(sub 0) = crazy E thing times l(x) times b(x) |
| finite rate of increase (λ) | ratio measuring the proportional change in population size from one time step to the next in a discrete model of exponential population growth |
| density dependence | model in which the instantaneous birth and death rates (b and d) are influenced by the density (or size) of the population. Crowding and carrying capacity |
| density independence | population processes are not affected by the current density of the population. Population can grow exponentially because the rates do not depend on how large the population is. |
| r (approximate) | ln(Rsub0) / G |
| l(x) | S(x) / Ssub0 |
| g(x) | lx+1 / lx |
| Rsub0 | E(lx)(bx) |
| G | E(x)(lx)(bx) / E(lx)(bx) |
| Tsub double | ln(2) / r |
| Exponential growth equation | Nsubt = (Nsub0)(e^rt) |
| Logistic Growth equation | dN/dT = rN(1-N/K) |
| r is maximum when | N = K/2 |
| P = 0.005 | means that there is a 0.5% chance of obtaining the observed results if the null hypothesis were true. |
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