inferential statistics Flashcards
What is statistical testing?
Statistical testing is a method used to determine whether the results of a study are due to chance or whether they reflect a true effect in the population. It involves calculating probabilities and using statistical tests to analyze data.
What is the purpose of the sign test?
The sign test is used to determine whether there is a significant difference between two related variables or conditions, particularly when the data is ordinal or non-parametric. It is often used when the data involves paired observations.
When do you use the sign test?
The sign test is used when you have paired data (e.g., before and after measures), the data is ordinal or not normally distributed, and you are testing for differences in the direction of change (positive or negative).
How do you calculate the sign test?
To calculate the sign test, count the number of positive and negative differences between the pairs. The smaller of these two numbers is the test statistic, which is compared to a critical value from the sign test table.
What is probability in statistical testing?
Probability is the likelihood that a particular event will occur. In statistical testing, it is used to determine the significance of the results by comparing the observed probability with a predetermined significance level (usually 0.05).
What is the significance level in statistical testing?
The significance level (denoted as alpha, often 0.05) is the threshold at which the results of a test are considered statistically significant. If the probability of the result occurring by chance is less than the significance level, the result is considered significant.
What are Type I and Type II errors?
A Type I error occurs when a true null hypothesis is rejected (a false positive), while a Type II error occurs when a false null hypothesis is not rejected (a false negative). Both errors can affect the validity of a study’s conclusions.
How do you interpret statistical significance using critical values?
To interpret statistical significance, compare the calculated test statistic to the critical value from the statistical table at a given significance level (e.g., 0.05). If the test statistic exceeds the critical value, the result is statistically significant.
What factors affect the choice of statistical test?
Factors affecting the choice of statistical test include the level of measurement (nominal, ordinal, interval), the experimental design (e.g., related vs unrelated), and whether the data involves differences (e.g., comparing means) or correlations (e.g., relationships between variables).
When do you use Spearman’s rho?
Spearman’s rho is used to assess the strength and direction of a monotonic relationship between two variables. It is used when the data is ordinal or when assumptions of Pearson’s r (such as normality) are violated.
When do you use Pearson’s r?
Pearson’s r is used to measure the strength and direction of a linear relationship between two continuous variables, assuming the data is normally distributed and the variables are measured at the interval or ratio level.
When do you use the Wilcoxon test?
The Wilcoxon test is a non-parametric test used to compare two related samples when the data is ordinal or non-normally distributed. It is used when you want to test for differences in ranks between two related groups.
When do you use the Mann-Whitney test?
The Mann-Whitney test is used to compare two independent samples when the data is ordinal or non-normally distributed. It is a non-parametric alternative to the independent t-test.
When do you use the related t-test?
The related t-test (paired samples t-test) is used to compare the means of two related groups (e.g., before and after treatment) when the data is interval/ratio and normally distributed.
When do you use the unrelated t-test?
The unrelated t-test (independent t-test) is used to compare the means of two independent groups when the data is interval/ratio and normally distributed.
When do you use the Chi-Squared test?
The Chi-Squared test is used to assess whether there is a significant association between two categorical variables. It is often used in contingency table analysis to test for independence.
How do you choose between a difference test and a correlation test?
Use a difference test (e.g., t-test or Wilcoxon) when comparing the means or distributions of two or more groups. Use a correlation test (e.g., Pearson’s r or Spearman’s rho) when assessing the relationship between two continuous variables.
What is the difference between related and unrelated designs?
Related designs involve testing the same participants under different conditions (e.g., repeated measures or paired samples). Unrelated designs involve testing different participants in each group (e.g., independent samples).
