Introduction to Inferential Statistics

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28 Terms

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Inferential Statistics

Make conclusions about a larger population of values by making measurements in a smaller sample

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inferential stats uses

probability to make predictions

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There is always some data that is the result of ___ alone!

chance

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Parametric Stats

Groups of stats related to making distributional assumptions about a population

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Assume that your data comes from a population that follows certain ____

distribution patterns

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parametric stats are More powerful than non-parametric b/c we can more likely detect a ___ or difference in data

true relationship

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Non-parametric Stats

Non-normal distributions of populations

e.g. skewed distributions in handedness (left vs. right)

extremely smal sample sizes

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Non-parametric tests include:

Chi squared (X2) analysis

Wilcoxin-ranking

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Overview of Using Inferential Statistics in Research

1.State the hypothesis (research and null)

2.Define significance level (α)

â—¦Determine cut-offs for whether or not something is significant

3.Determine if data meets assumptions for chosen test statistic

â—¦Normal distribution vs. non-normal

4.Compute the parameters (mean, SD)

5.Compute the test statistic and obtain p-value

â—¦Filling data into statistical equations and determine p-value

6.Determine if result is statistically significant and clearly state a conclusion

â—¦p-value determines the statistical significance

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Research hypothesis (Ha)

â—¦Best guess answer to the research question

◦The expectation that is to be tested – “when you run the test, what do you expect to occur”

â—¦aka alternative hypothesis

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Null hypothesis (H0)

â—¦States that there will be no relationship or difference between variables of interest (independent and dependent variable)

◦Null hypothesis is the “no-effect” statement

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If your research hypothesis is that there will be a difference between two groups, then the ____ is that there is no difference between the groups

null hypothesis

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Statistically, you are always testing the ___ hypothesis

null

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Non-directional

}No prediction of direction being higher or lower

Yes/no scenario

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Directional

Measures the direction of variation of two variables

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Serum cholesterol is not affected by ingesting eggs

null hypothesis

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Serum cholesterol is increased after the ingestion of eggs

directional

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Serum cholesterol is affected by the ingestion of eggs

non-directional

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If data show that it is unlikely that chance is causing observed differences, then this is a ____

significant difference

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Results of an analysis are unlikely to be due to chance means that

Results are statistically significant

Reject the null hypothesis (H0)

Might accept the alternative hypothesis (Ha)

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if there is No significant difference:

May not be enough evidence to reject the H0

Does not necessarily mean that there is no true effect occurring; just means the data is too weak to substantiate the effect

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P-Value

Probability value

A statistical measure used to determine the probability that an observed outcome is the result of chance

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The lower the p-value, the more likely your intervention or treatment was causing ”___” between your variables

significant difference

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Alpha-Levels (α)

Cutoff or threshold value that determines if the level of the p-value is going to be defined as “statistically significant”

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The most common alpha level is

0.05

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◦If p≤0.05 = ___

â—¦If p>0.05 = ___

◦If p≤0.05 = statistical significance!

â—¦If p>0.05 = no effect observed

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Inferential statistics are common in hard-science research such as

â—¦Determine efficacy of a drug

â—¦Evaluate relationships between a gene mutation and disease

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In the medical lab, inferential statistics are used for method validations like

â—¦Accuracy

â—¦Precision

â—¦Comparison of methods for accuracy/precision

â—¦Linearity