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The maximum likelihood estimate is defined as the parameter value that corresponds to the peak (maximum) of the likelihood curve. It is the value of the parameter that gives the highest likelihood for the observed data.

Conditions: A likelihood function $L(p)$ over candidate parameter values is available.; The data are fixed.

The maximum likelihood estimate is defined as the parameter value that corresponds to the peak (maximum) of the likelihood curve. It is the value of the parameter that gives the highest likelihood for the observed data.

Conditions: A likelihood function $L(p)$ over candidate parameter values is available.; The data are fixed.

Among the tested parameter values, $p=0.7$ yields the highest likelihood for the observed data. The likelihood measures how well each candidate value of $p$ explains the fixed observation of 7 heads in 10 flips.

Conditions: The observed data are fixed at 7 heads in 10 flips.; The candidate values tested are $p=0.3, 0.5,$ and $0.7$.

Among the tested parameter values, $p=0.7$ yields the highest likelihood for the observed data. The likelihood measures how well each candidate value of $p$ explains the fixed observation of 7 heads in 10 flips.

Conditions: The observed data are fixed at 7 heads in 10 flips.; The candidate values tested are $p=0.3, 0.5,$ and $0.7$.