Question

In: Statistics and Probability

3. Short answer and definitions: Illustrate with examples and answer any 15 of the following: confounding...

3. Short answer and definitions: Illustrate with examples and answer any 15 of the following: confounding sampling error statistical inference research hypothesis research study status variable central limit theorem confidence interval point estimate standard error of the mean t distribution ? (alpha) ? (beta) directional hypothesis non-directional hypothesis null hypothesis (H0) statistical hypothesis statistically significant two tailed test standard error of the difference test statistic between two means two sample t- test z test chi- square test X2 test of independence expected frequency non-parametric statistic analysis of variance post hoc comparison Scheffe method

Solutions

Expert Solution

1)

In statistics, a confounder (also confounding variable, confounding factor or lurking variable) is a variable that influences both the dependent variable and independent variable causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations

2)

A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population. Sampling is an analysis performed by selecting a number of observations from a larger population, and the selection can produce both sampling errors and non-sampling errors.

3)

Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.[1] Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population.

4)

A research hypothesis (H1) is the statement created by researchers when they speculate upon the outcome of a research or experiment.

Every true experimental design must have this statement at the core of its structure, as the ultimate aim of any experiment.


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