def estimateNormal(sampleData, performLowerBoundCheck=True):
params = {<EOL>"<STR_LIT:name>": "<STR_LIT>",<EOL>"<STR_LIT>": numpy.mean(sampleData),<EOL>"<STR_LIT>": numpy.var(sampleData),<EOL>}<EOL>if performLowerBoundCheck:<EOL><INDENT>if params["<STR_LIT>"] < <NUM_LIT>:<EOL><INDENT>params["<STR_LIT>"] = <NUM_LIT><EOL><DEDENT>if params["<STR_LIT>"] < <NUM_LIT>:<EOL><INDENT>params["<STR_LIT>"] = <NUM_LIT><EOL><DEDENT><DEDENT>if params["<STR_LIT>"] > <NUM_LIT:0>:<EOL><INDENT>params["<STR_LIT>"] = math.sqrt(params["<STR_LIT>"])<EOL><DEDENT>else:<EOL><INDENT>params["<STR_LIT>"] = <NUM_LIT:0><EOL><DEDENT>return params<EOL>
:param sampleData:
:type sampleData: Numpy array.
:param performLowerBoundCheck:
:type performLowerBoundCheck: bool
:returns: A dict containing the parameters of a normal distribution based on
    the ``sampleData``.