If you have a machine learning classifier, it can make guesses about whether a person randomly selected from a population has a certain/trait
Even if the classifier is not perfectly accurate, its predictions still have informational value, allowing you to narrow your confidence of what the population looks like
Use the following program to estimate the likely bounds of the proportion within the population if you have predictions made by a classifier and know its error rate
A digital black/whiteboard to allow for seemless switching between slides and demonstrations
Use the touchscreen function on the presentation device or a pen display (e.g, Wacom tablet) to treat the screen like a drawing board.
Different keyboard keys map to different actions (e.g., DEL = Clear screen, ctrl+z = Undo, E = Eraser
Receive recommendations for items into include in your personality scale
AI Natural language processing models analyze sample input items and recommend new ones for your scale that that will correlate with them
Receive both positive and negative(reverse) coded items
Translate your effect sizes into terms that are more easily understood by a a broader audience
Choose your study design, enter your effect size, and see how to describe the results in simpler terms.
Before conducting a study, there are many important considerations to make.
Your ability to draw conclusions from data is limited by several factors such as reliability, validity, power, etc.
Determine the extent that people in a room prefer to sit next other people who are similar to them. Create a seating chart in a spreadsheet, and the program will calculate whether the number of people sitting next to similar others in that room is unlikely if people were simply choosing their seats randomly.
