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Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests. I'll explain the one-tailed use and then discuss how to do it for two-tailed tests.

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

In fact the information to double it is already in the formula in your question:

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

When your t statistic is between two tabulated values, it is possible to get an approximation to the p-value via interpolation but it's not necessary in this case (and I wouldn't spend the extra time in an exam).

Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests. I'll explain the one-tailed use and then discuss how to do it for two-tailed tests.

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

In fact the information to double it is already in the formula in your question:

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

When your t statistic is between two tabulated values, it is possible to get an approximation to the p-value via interpolation but it's not necessary in this case (and I wouldn't spend the extra time in an exam).

Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests. I'll explain the one-tailed use and then discuss how to do it for two-tailed tests.

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

It is possible to get an approximation to the p-value via interpolation but it's not necessary in this case (and I wouldn't spend the extra time in an exam).

Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests. I'll explain the one-tailed use and then discuss how to do it for two-tailed tests.

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

In fact the information to double it is already in the formula in your question:

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

When your t statistic is between two tabulated values, it is possible to get an approximation to the p-value via interpolation but it's not necessary in this case (and I wouldn't spend the extra time in an exam).

Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

Also, regarding the t-table I linked, is this table only for one-tailed t-tests?

Directly it's for one-tailed but you can use it with two-tailed tests. I'll explain the one-tailed use and then discuss how to do it for two-tailed tests.

There is no value close to 0.43 in the row of df = 26 on the t-table, so what do I do?

That sort of table is able to give bounds on the p-value. That's sufficient to know whether to reject or not.

$$ \begin{array}{r r r r r|r} \hline t_{0.1}&t_{0.05}&t_{0.025}&t_{0.010}& t_{0.005}&\text{df}\\ \hline \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ 1.315& 1.706& 2.056& 2.479& 2.779& 26\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \end{array} $$

For example if your t-value was 1.5, which is between 1.315 and 1.706, you know that the one tailed p-value is between 0.1 and 0.05.

With $t=0.433$ you'd only be able to say that the one-tailed p-value was greater than $0.1$ (and for any typical significance level that means you don't reject $H_0$).

what do I do if I need to do a two-tailed test?

Double the one-tailed p-value (or with this table, double the bounds you identify).

So if you had $|t|=1.5$ you'd say the two-tailed p-value was between $0.2$ and $0.1$.

If you had $|t|=0.433$ you'd say that the two-tailed p-value was greater than $0.2$.

It is possible to get an approximation to the p-value via interpolation but it's not necessary in this case (and I wouldn't spend the extra time in an exam).