Match the term with the definition below:

$(1)$ Alternative hypothesis blank

$(2)$ Categorical data blank

$(3)$ Classification analyses blank

$(4)$ Confirmatory data analysis blank

$(5)$ Data deception blank

$(6)$ Data ordering blank

$(7)$ Data overfitting blank

$(8)$ Effect size blank

$(9)$ Emphasis blank

$(10)$ Ethical presentation blank

$($a$)$ in design, making a visualization easy to interpret and understand.

$($b$)$ techniques that identify various groups and then try to classify a new observation into one of those groups.

$($c$)$design rule suggesting that a viz should not contain too much or too little, but just the right amount of data.

$($d$)$avoiding the international or unintentional use of deceptive practices that can alter the user's understanding of the data being presented.

$($e$)$the intentional arranging of visualization items in a way to produce emphasis.

$($f$)$ a proposed explanation worded in the form of an inequality, meaning that oone of the two concepts, ideas, or groups will be greater or less than the other concept, idea, or group.

$($g$)$ any visual representation of data, for example, graphs, diagrams, or animations.

$($h$)$testing a hypothesis and providing statistical evidence of the likelihood that the evidence refutes or supports a hypothesis

$($i$)$data items that take on a limited number of assigned values to represent different groups.

$($j$)$ a graphical depiction of information, designed with or without an intent to deceive, that may create a belief about the message and$/$or its components, which varies from the actual message.

$($k$)$a quantitative measure of the magnitude of the effect

$($l$)$when a model is designed to fit training data very well but does not predict well when applied to other data sets

$($m$)$in design, making it easy to know what is most important