In mathematics indicator has a very precise meaning:

In mathematics, an indicator function or a characteristic function is a function defined on a set $X$ that indicates membership of an element in a subset $A$ of $X$, having the value $1$ for all elements of $A$ and the value $0$ for all elements of $X$ not in $A$. It is usually denoted by a symbol $1$ or $I$, sometimes in boldface or blackboard boldface, with a subscript describing the set.

While in case of goodness of fit we are talking rather about measures, statistics, or tests, e.g.

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-squared test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.

Notice that goodness of fit statistics measure how well does your morel fit the data. In some cases you can conduct a statistical test to check this. However they never indicate (i.e. show, point, demonstrate) that your model fits -- such decisions are always done subjectively, based on the available measures since it is never a yes/no kind of situation.

You might been thinking about "estimator" (as "estimator" and "indicator" sound similar), but this would also be incorrect since you are not estimating any property of your data.

So you can call them "measures", sometimes they are called "statistics", and if you were talking solely about tests (which $R^2$ is obviously not), then about "tests".

In mathematics indicator has a very precise meaning:

In mathematics, an indicator function or a characteristic function is a function defined on a set $X$ that indicates membership of an element in a subset $A$ of $X$, having the value $1$ for all elements of $A$ and the value $0$ for all elements of $X$ not in $A$. It is usually denoted by a symbol $1$ or $I$, sometimes in boldface or blackboard boldface, with a subscript describing the set.

While in case of goodness of fit we are talking rather about measures, statistics, or tests, e.g.

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-squared test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.

Notice that goodness of fit statistics measure how well does your morel fit the data. In some cases you can conduct a statistical test to check this. However they never indicate (i.e. show, point, demonstrate) that your model fits -- such decisions are always done subjectively, based on the available measures since it is never a yes/no kind of situation.

You might been thinking about "estimator" (as "estimator" and "indicator" sound similar), but this would also be incorrect since you are not estimating any property of your data.

So you can call them "measures", sometimes they are called "statistics", and if you were talking solely about tests (which $R^2$ is obviously not), then about "tests".

In mathematics indicator has a very precise meaning:

In mathematics, an indicator function or a characteristic function is a function defined on a set $X$ that indicates membership of an element in a subset $A$ of $X$, having the value $1$ for all elements of $A$ and the value $0$ for all elements of $X$ not in $A$. It is usually denoted by a symbol $1$ or $I$, sometimes in boldface or blackboard boldface, with a subscript describing the set.

While in case of goodness of fit we are talking rather about measures, statistics, or tests, e.g.

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-squared test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.

You might been thinking about "estimator" (as "estimator" and "indicator" sound similar), but this would also be incorrect since you are not estimating any property of your data.

So you can call them "measures", sometimes they are called "statistics", and if you were talking solely about tests (which $R^2$ is obviously not), then about "tests".

In mathematics indicator has a very precise meaning:

In mathematics, an indicator function or a characteristic function is a function defined on a set $X$ that indicates membership of an element in a subset $A$ of $X$, having the value $1$ for all elements of $A$ and the value $0$ for all elements of $X$ not in $A$. It is usually denoted by a symbol $1$ or $I$, sometimes in boldface or blackboard boldface, with a subscript describing the set.

While in case of goodness of fit we are talking rather about measures, statistics, or tests, e.g.

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question. Such measures can be used in statistical hypothesis testing, e.g. to test for normality of residuals, to test whether two samples are drawn from identical distributions (see Kolmogorov–Smirnov test), or whether outcome frequencies follow a specified distribution (see Pearson's chi-squared test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares.

Notice that goodness of fit statistics measure how well does your morel fit the data. In some cases you can conduct a statistical test to check this. However they never indicate (i.e. show, point, demonstrate) that your model fits -- such decisions are always done subjectively, based on the available measures since it is never a yes/no kind of situation.

You might been thinking about "estimator" (as "estimator" and "indicator" sound similar), but this would also be incorrect since you are not estimating any property of your data.

So you can call them "measures", sometimes they are called "statistics", and if you were talking solely about tests (which $R^2$ is obviously not), then about "tests".