Inferential Statistics Flashcards

Flashcards for reviewing inferential statistics concepts.

Inferential Statistics

Making predictions or conclusions about a larger group of data by analyzing a smaller sample of it.

Hypothesis Testing

Used to evaluate assertions about a population based on unknown parameters or distribution properties.

Relationship (Statistical Method)

Examines connections between variables.

Comparison (Statistical Method)

Compares differences between groups.

Regression (Statistical Method)

Predicts outcomes, analyzes linear relationships, & checks for correlation.

Statistical Hypothesis

A claim or assumption about one or more populations.

Null Hypothesis (Ho)

The statement being tested; usually represents the idea the researcher doubts or wants to challenge.

Alternative Hypothesis (Ha)

What the researcher believes to be true and aims to support with evidence.

Type I Error (False Positive)

Rejecting the null hypothesis when it is actually true.

Type II Error (False Negative)

Failing to reject the null hypothesis when it is actually false.

Level of Significance (alpha)

The maximum chance of making a Type I error that the researcher is willing to accept.

One-Tailed Test

Alternative hypothesis specifies a directional difference for the parameter

Two-Tailed Test

Alternative hypothesis does not specify a directional difference for the parameter

Critical Region (Rejection Region)

Values that lead to rejecting the null hypothesis

Acceptance Region

Values that lead to not rejecting the null hypothesis

Critical Value

The value that separates the critical & acceptance regions

Importance of Testing for Normality

To determine whether the data follows a normal distribution.

Qualitative Variables Level of Measurement and Type of Test

Nominal & Ordinal, use non-parametric test

Quantitative Variables Level of Measurement and Type of Test

Interval & Ratio, test for normality first, if normal use parametric tests, if not normal use non-parametric tests

Negative Skewness

skewed to the left

Positive Skewness

skewed to the right

Shapiro-Wilk Test

A statistical Test used to determine normality

Shapiro-Wilk W Statistics

is a numerical value that measures now closely your data matches a normal distribution RANGE: 0-1

Shapiro-Wilk P-Value

the probability that your data is not Significantly different from a normal distribution

Test Statistic

The number you get after doing some calculations using your sample data

Critical Value

A cutoff value that defines the rejection region based on significance level (a)

T-test

Used to compare means when population variance is unknown and sample size is small (n≤30)

Z-test

Used to compare when population variance is known & sample size is large (n > 30)

Independent Samples T-Test

Compare 2 different groups, ex. section A vs. section B

One Sample T-Test

Compares a group and a known mean, ex. Class average vs. national average

Paired Samples T-Test

Compares same group ex. Before review VS. After review

F-Test (for variance comparison)

compare two variances

F-test Null Hypothesis

There are 2 data sets have equal variance

F-test Alternative Hypothesis

There are 2 data sets have unequal variance

Analysis of Variance (ANOVA)

A statistical method used to compare the means of 3 or more groups to see if they are significantly different from each other

One Way ANOVA

Values of the categorical factor divide the continuous data into groups. Independent variable (categorical), dependent variable (continuous)

Two Way ANOVA W/O Replication

Compare a group of individuals performing more than one task. 2 factors (independent variable), only one observation per group combination

Two Way ANOVA W/ Replication

Studying 2 independent variables, more than one sample (replication) for each combination of those factor IVs, check for Main effects of each factor, Interaction between Factors

Pearson's Correlation

Statistical measure that evaluates the strength & direction of a linear relationship between 2 continuous variables

Interpretation of Pearson's r

Ranges from -1 to +1, +1: Perfect positive correlation, 0: No linear correlation, -1: Perfect Negative correlation

Point Biserial

Measures the Strength & direction of association between continuous variable (eg. test score) and binary (dichotomous) variable (eg.gender)