Test
Chapter 8
Este test tiene tipos de pregunta que se abren en el reproductor de siempre.
Preguntas del test (31)
1A [blank_start]deductive[blank_end] argument is intended to provide [blank_start]logically[blank_end] conclusive support for its [blank_start]conclusion[blank_end]. An [blank_start]argument[blank_end] can be either valid or invalid, sound or unsound
- Adeductive
- Blogically
- Cconclusion
- Dargument
2An [blank_start]inductive[blank_end] argument is intended to supply only [blank_start]probable[blank_end] support for its [blank_start]conclusion[blank_end], earning the label of "strong" if it [blank_start]succeeds[blank_end] in providing such support and "weak" if it [blank_start]fails[blank_end]
- Ainductive
- Bprobable
- Csucceeds
- Dfails
- Econclusion
3The conclusion of an [blank_start]inductively strong[blank_end] argument is simply more likely to be [blank_start]true[blank_end] than not. If the argument’s [blank_start]premises[blank_end] are true, it is said to be [blank_start]cogent[blank_end]
- Ainductively strong
- Bpremises
- Ccogent
- Dtrue
4An [blank_start]inductively strong[blank_end] argument can never [blank_start]guarantee[blank_end] that the conclusion is true but it can render the conclusion [blank_start]probably true[blank_end], even highly likely to be [blank_start]true[blank_end]
- Ainductively strong
- Bguarantee
- Cprobably true
- Dtrue
5[blank_start]Inductive[blank_end] arguments cannot give us [blank_start]certainty[blank_end], but they can give us high levels of [blank_start]probability[blank_end]-high enough at least to help us acquire [blank_start]knowledge[blank_end] in everything from physics to birdwatching
- Acertainty
- Bprobability
- Cknowledge
- DInductive
6[blank_start]Deductive[blank_end] logic is the invisible [blank_start]framework[blank_end] on which much of our [blank_start]reasoning[blank_end] hangs and the solid bond that holds together the [blank_start]logical[blank_end] frameworks of mathematics, computer science, and other theoretical or [blank_start]abstract disciplines[blank_end]
- ADeductive
- Bframework
- Creasoning
- Dlogical
- Eabstract disciplines
7Inductive reasoning gives us most of what we know about the [blank_start]empirical[blank_end] workings of the world, allowing us in [blank_start]science[blank_end] and in ordinary experience to soar [blank_start]reliably[blank_end] from what we know to what we don't
- Aempirical
- Breliably
- Cscience
8Inductive argument allows us to reason "[blank_start]beyond the evidence[blank_end]" from bits of what is already know, to [blank_start]conclusions[blank_end] about what those bits suggest is probably [blank_start]true[blank_end].
- Abeyond the evidence
- Bconclusions
- Ctrue
9Enumerative Induction is an [blank_start]inductive argument[blank_end] pattern in which we reason from [blank_start]premises[blank_end] about [blank_start]individual[blank_end] members of a group to [blank_start]conclusion[blank_end] about the group as a [blank_start]whole[blank_end]
- Ainductive argument
- Bpremises
- Cindividual
- Dconclusion
- Ewhole
10[blank_start]Enumerative induction[blank_end] is a way of reasoning that is both [blank_start]natural[blank_end] and useful
- AEnumerative induction
- Bnatural
11[blank_start]Enumerative induction[blank_end] has this form:
X per cent of the observed members of group A have property P. Therefore, X per cent of all members of group A probably have property P- AEnumerative induction
12ln enumerative induction, the whole collection of [blank_start]individuals[blank_end] under study is called [blank_start]target group[blank_end]
- Aindividuals
- Btarget group
13In enumerative induction, the [blank_start]observed[blank_end] members of the target group is called [blank_start]sample members[blank_end]
- Aobserved
- Bsample members
14Relevant property has another name "[blank_start]property in question[blank_end]"
In enumerative induction, a [blank_start]property[blank_end], or [blank_start]characteristic[blank_end], that is of interest in the [blank_start]target group[blank_end] is called relevant property- Aproperty in question
- Bproperty
- Ccharacteristic
- Dtarget group
15Induction argument vary in its strength in the [blank_start]degree of support[blank_end] that the premises as well as on how much is claimed in the [blank_start]conclusion[blank_end].
- Adegree of support
- Bconclusion
16The [blank_start]strength[blank_end] of the induction argument depends on the [blank_start]premises[blank_end] as well as on how much is claimed in the [blank_start]conclusion[blank_end].
- Apremises
- Bstrength
- Cconclusion
17An enumerative [blank_start]inductive[blank_end] argument can [blank_start]fail[blank_end] to be strong in [blank_start]two[blank_end] major ways: its sample can be (1) [blank_start]too small[blank_end] or (2) [blank_start]not representative[blank_end]
- Afail
- Binductive
- Ctwo
- Dtoo small
- Enot representative
18The fallacy of drawing a [blank_start]conclusion[blank_end] about a [blank_start]target group[blank_end] on the basis of too small a [blank_start]sample[blank_end] is called [blank_start]hasty generalization[blank_end] .
- Aconclusion
- Btarget group
- Csample
- Dhasty generalization
19People regularly make [blank_start]hasty generalization[blank_end], when dealing with all sorts of [blank_start]enumerative inductive evidence[blank_end]-political polls, consumer opinion surveys, scientific studies (especially medical research), quality-control checks, anecdotal reports, and many others
- Ahasty generalization
- Benumerative inductive evidence
20The [blank_start]more[blank_end] homogeneous a target group is in traits relevant to the property in question, the [blank_start]smaller[blank_end] the sample can be; the less [blank_start]homogeneous[blank_end], the larger the [blank_start]sample[blank_end] should be.
- Amore
- Bsmaller
- Chomogeneous
- Dsample
21In enumerative induction, a [blank_start]sample[blank_end] that resembles the [blank_start]target group[blank_end] in all relevant ways is called [blank_start]representative sample[blank_end]
- Atarget group
- Brepresentative sample
- Csample
22To be truly [blank_start]representative[blank_end], the sample must be like the [blank_start]target group[blank_end] by (1) having all the same [blank_start]relevant characteristics[blank_end] and (2) having them in the [blank_start]same proportions[blank_end] that the [blank_start]target group[blank_end] does
- Arepresentative
- Btarget group
- Crelevant characteristics
- Dsame proportions
- Etarget group
23A [blank_start]sample[blank_end] that does not properly represent the [blank_start]target group[blank_end] is called [blank_start]biased[blank_end] sample
- Asample
- Btarget group
- Cbiased
24Enumerative inductions reach a high level of [blank_start]sophistication[blank_end] in the form of [blank_start]opinion polls[blank_end] conducted by professional [blank_start]polling organizations[blank_end].
- Asophistication
- Bopinion polls
- Cpolling organizations
25[blank_start]Opinion polls[blank_end] are used to arrive at [blank_start]generalizations[blank_end] about everything
- AOpinion polls
- Bgeneralizations
26Opinion polls are still essentially [blank_start]inductive arguments[blank_end] (or the [blank_start]basis[blank_end] of inductive arguments) and must be [blank_start]judged[blank_end] accordingly
- Ainductive arguments
- Bbasis
- Cjudged
27Opinion polls should (1) be [blank_start]strong[blank_end] and (2) have true [blank_start]premises[blank_end]
- Astrong
- Bpremises
28Any opinion poll worth believing must (1) use a sample that is [blank_start]large[blank_end] enough to represent the [blank_start]target population[blank_end] accurately in all the [blank_start]relevant population[blank_end] features and (2) generate [blank_start]accurate data[blank_end] (that is, the results must correctly [blank_start]reflect[blank_end] what they purport to be about).
- Alarge
- Btarget population
- Crelevant population
- Daccurate data
- Ereflect
29A poll can fail to meet latter requirement through [blank_start]data-processing errors[blank_end], botched [blank_start]polling interviews[blank_end], poorly [blank_start]phrased questions[blank_end], and the like
- Adata-processing errors
- Bpolling interviews
- Cphrased questions
30In [blank_start]national[blank_end] polling, samples need not be [blank_start]enormous[blank_end] to be accurate reflections of the [blank_start]larger target population[blank_end]
- Aenormous
- Blarger target population
- Cnational
31[blank_start]Modern sampling[blank_end] procedures used in national polls can produce [blank_start]representative samples[blank_end] that are surprisingly [blank_start]small[blank_end]
- AModern sampling
- Brepresentative samples
- Csmall
Información
- Descripción
- 1 Philosophy Test sobre Chapter 8, creado por Zinia Ahmed el 12/12/2017.
- Autor
- Zinia Ahmed
- Idioma
- English
- Visitas
- 1
- Historial
- Creado el 12 dic 2017 · actualizado el 13 dic 2017
- Etiquetas
- Valoración
- Aún sin valoraciones Valora este test