Yankel said:If I get you right, for each kid you have two samples, one for each tool. Both numbers are continuous and you want to compare the tools ? If so, paired t test
mermaidrunner said:Yes, that is correct. Each kid has a result from each tool. There were 51 total kids tested. My main concern is that these screening tools aren't scored on a numerical scale. It is strictly pass or fail - is that going to have an impact on using the paired t test? Should I be looking at something more nonparametrical?
I like Serena said:Hi mermaidrunner. Welcome to MHB!
That sounds like a two-proportion z-test, pooled for $H_0\colon p_1=p_2$.
See for instance this wiki page.
To determine the best statistical test for your data, you must first consider the type of data you have (e.g. numerical, categorical, etc.), the number of variables being analyzed, and the research question being asked. Consulting with a statistician or using online resources, such as statistical software, can also help guide you in selecting the appropriate test.
Data preprocessing involves cleaning, organizing, and transforming raw data into a format suitable for analysis. It is important because it helps to ensure the accuracy and reliability of the results by eliminating errors and irrelevant data. It also helps to identify any missing data or outliers that may affect the analysis.
Handling missing data depends on the type of analysis being performed and the reason for the missing values. If the data is missing at random, it can be ignored or replaced with the mean or median of the variable. If the data is missing systematically, it may be necessary to use imputation methods, such as regression or k-nearest neighbors, to fill in the missing values.
Descriptive statistics involves summarizing and describing the characteristics of a dataset, such as measures of central tendency and variability. Inferential statistics, on the other hand, involves using sample data to make inferences or predictions about a larger population. It allows us to draw conclusions or test hypotheses based on the data.
There are many ways to visually represent data, including bar graphs, pie charts, histograms, scatter plots, and box plots. The type of graph or chart used depends on the type of data being analyzed and the research question being asked. It is important to choose a visual representation that accurately and clearly communicates the data to the audience.