inferential statistics

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what is a CI

a quantitative measure of how certain we can be that a specific interval contains the true parameter

what is an interval

a range of values around a point estimate made on a sample

The best inference of a population mean is? what about a population SD

a sample mean. a sample SD

Inferential statistical procedures are used to

make inductive inferences of population parameters using sample statistics

Probability of observed effect being due to sampling variation (chance) alone is too high to rule out Ho

then fail to reject and results are not significantly significant

whats the purpose of a hypothesis test

to decide which of at least two hypotheses are correct

Interval estimates quantify

level of uncertainty regarding true value of a parameter

types of effects

--Response magnitude to experimental manipulation --Degree of correlation between variables (strength of a trend) --Difference between statistics (eg means being compared)

5 concepts of an inference

1. parameters(point& interval) 2. uncertainty(margin of error and CI's) 3. effect size 4. stat significance(p value) 5. power

most common CI

95%

why is There is always uncertainty about how close a sample statistic is to the unknown population parameter.

A point estimate, by itself, provides no information on the degree of uncertainty. Due to sampling error we can only infer from samples to population parameters.

what does an interval measure

CI and margin of error

p value is close to what

CI, they work together and you cant have one without the other

what is the presumption of innocence

Defendant may be innocent or guilty but is presumed innocent until proven guilty. The Ho assumes innocence = default position.

false negative solution?

Making an incorrect decision that a person is not sick when they are -increase test power

false positive solution?

Making an incorrect decision that a person is sick when they are not -use more restrictive p values

Must a statistically significant result be of biological or scientific importance?

No. There is a difference between concepts of statistical significance and biological or scientific importance

the length of CI is

a measure of our level of uncertainty about a parameter ------ *precision of inference*

what does statistically significant effect mean

an effect is unlikely if Ho is really true

a powerful test provides evidence to do what to a false Ho

be able to reject the false Ho

an H test is like a

criminal trial-- innocent(Ho) until proven guilty(Ha)

Less sample variance means

easier it is to show that a null hypothesis is false///reduces the p value

the bigger the CI the

more uncertainty and less precise inference

statistically significant result is

one with a p value of .05 or less

types of estimation

point(mean, variance, SD and proportions) and interval(CI and margin of error)

probability can be used to quantify

quantify how sure (or unsure) one is about the truth of a proposition or statement.

Interval estimation is a

range of values around a point estimate that, with a specified level of certainty or probability, enclose the true parameter.

All probabilities are

ratios. It is essential to define both the numerator (particular event occurs) and denominator (number of all possible events, given conditions).

what is probability

the chance that an event will happen relative to all possible events that could happen - under defined conditions.

the smaller CI is

the less uncertainty and more percise our inference

the greater the power,

the more reliable a decision to reject of not we can make

define p value

the probability of results assuming Ho is true

what does heterogeneity mean

variation, scatter

what is power

decision reliability---the probability of rejecting a false Ho

steps of H test

define problem define testable hypothesis design test test it, collect data, analyze decide to reject or fail to reject

statistical significance does not equal

effect size

ways we use inferential statistics

estimation and hypothesis testing

Using inferential statistics, you are trying to reach a conclusion that

extends beyond immediate data alone - to make inductive inferences about population parameters based on probability.

test power helps to make

good decisions

the less a p value is, the

greater chance Ho is not true

what is margin of error

interval length of that portion of the 95% CI on either side of a point estimate.

larger sample size means what about uncertainty

less uncertainty and more precise

Less random variation (smaller sample SD) means

less uncertainty of inference = better precision.

why are inferential statistics necessary

only when a quantitative inductive inference about population parameter(s) needs to be made

five concepts we are seeking in a test of a null hypothesis

parameter estimates precision effect size p value of Ho power

inferential statistics is what type of inference

probabilistically-based inductive inference

why are descriptive statistics necessary

whenever simple quantitative description is required

what is an effect size

= the quantitative magnitude or "strength" of an observed effect, or a relationship of two variables, or the difference in values of statistics between groups being compared

what is a point estimate

A single value for a sample summary statistic representing the "best guess" value of a parameter.

what is a model

All probability statements are based on a set of assumptions or conditions ex-under these conditions, the prob of x is y%

The p-value is a

a measure of the strength of evidence against a Ho.

Assuming Ho, the probability of observed effect occurring by chance alone is low (≤ 5%).

reject null and results are statistically significant

what is a hypothesis test

research to evaluate probable truth of a null hypothesis

a ________ is a point estimate of the ________ which is also called______

sample mean population mean parameter

what does CI length depend on

sample size (want this to be big) and random variation(SD)(want this to be small)

what can affect the p value

sample size, random variation and effect size

SD measures

scatter in data(dispersion)

whats general form of hypothesis testing

to reject of fail to reject Ho


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