Imagine you are a researcher trying to find a XXX XXXXXXXXXX to XXXXX depression.
XXXX XXXXX be a type X errors?
What XXXXX XX a type X XXXXX?
You XXX't XXXX XX tell XX the XXXXXXXX and null XXXXXXXXXX, just XXXX XX XXXX XXXXXXXX, (ex: XXXX you thought you found, and XXXX you XXXXXX XXX XXXX).
Even though hypothesis XXXXX are XXXXX XX be reliable, there XXX two types of errors that XXX XXXXX. XXXX 1 errors – often XXXXXXXXXXX with false XXXXXXXXX – happen in XXXXXXXXXX XXXXXXX XXXX XXX null hypothesis is XXXX but rejected. The XXXX hypothesis XX a XXXXXXX statement or XXXXXXX position that XXXXX is no XXXXXXXXXXXX between two XXXXXXXX phenomena. XXXX 2 XXXXXX XXXXXX when you XXXXXXXXXXXX assume that no XXXXXX has XXXX XXXXXXXX XXXXXXX a XXXXXXX version XXX a variation XXXXXXXX there actually is a XXXXXX.
XXXX XX XXX working on XXXXXXX a new medication XXXX XXXX XXXX to XXXXX depression, XX have XXX hypothesis.
XXX null hypothesis XXXX XX XXXX the new medication XXXXXXXXX XXXXX to prevent depression
Ho: XXX medication XXXXXXXXX helps to XXXXXXX XXXXXXXXXX
The XXXXXXXXX hypothesis XXXX XX that the new medication treatment XXXX XXX XXXX to XXXXXXX depression.
XX: New medication treatment doesn’t XXXX XX prevent XXXXXXXXXX.
Type X XXXXX in this case is that the new medication treatment XX unable XX treat depression and XXXXX the XXXXXXX XXXXX XXXX to it. Type 1 errors XXX XXXXXX due XX bad luck (the X% chance has played XXXXXXX you) or XXXXXXX you XXXX’t XXXXXXX XXX XXXX XXXXXXXX XXX XXXXXX size XXXXXXXXX set XXX your experiment.
The XXXX X XXXXX in this case is XXXX XXXXXXXX XXX XXX XXXXXXXXXX treatment did not cure XXXXXXXXXX, XXX it XXX considered that it XXX XXXX it. Similarly to XXXX X XXXXX, XXXX 2 XXXXX XXX lead to false assumptions and XXXX XXXXXXXX XXXXXX that can result in no XXXX
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