Since I've been doing some related work anyway, I might as well throw this in here. If anyone really, really, really needs to compute p or z values for whatever reason, Inquisit syntax for the standard normal CDF and its inverse is provided below. The code is based on Aludaat & Alodat (2008). More accurate algorithms are available, but I prefer these approximations because they're (a) relatively concise and (b) accurate enough for most purposes in psychology and related fields. Warning to unsuspecting readers: If you don't understand what this code does, chances are you don't need it. # Normal CDF and Inverse Normal CDF functions # This code is based on the approximations given in Aludaat & Alodat (2008). # Results should be accurate to about two decimal places, with larger deviations # for extreme values. This code is provided without any warranty.
# expressions.p_z computes p given z # expressions.z_p computes z given p
<expressions> / p_z = if(values.z>0) 0.5*(1+sqrt((1exp(sqrt(m_pi/8)*pow(values.z,2))))) else 10.5*(1+sqrt((1exp(sqrt(m_pi/8)*pow(values.z,2))))) / z_p = if(expressions.p_z>0.5) sqrt(ln(1pow(2*expressions.p_z1,2))/sqrt(m_pi/8)) else sqrt(ln(1pow(2*expressions.p_z1,2))/sqrt(m_pi/8)) </expressions>
<values> / z = 0.6 </values>
<text mytext> / items = ("z = <%values.z%>  p_z = <%expressions.p_z%>  z_p = <%expressions.z_p%>") </text>
<trial mytrial> / stimulusframes = [1=mytext] / validresponse = (anyresponse) </trial> Regards, ~Dave
