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((1-exp(-sqrt(m_pi/8)*pow(values.z,2))))) else

1-0.5*(1+sqrt((1-exp(-sqrt(m_pi/8)*pow(values.z,2)))))

/ z_p = if(expressions.p_z>0.5) sqrt(-ln(1-pow(2*expressions.p_z-1,2))/sqrt(m_pi/8)) else

-sqrt(-ln(1-pow(2*expressions.p_z-1,2))/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