You might be interested in two one-sided tests for equivalence (TOST), which allows you to pose a null hypothesis of difference by some tolerance (let's say, $\Delta$), and some preferred type I rejection rate $\alpha$, and reject this null in favor of evidence of equivalence (i.e. similarity)
If the typical one-sample t test for difference has $H^{+}_{0}: \mu- 196 = 0$ (the super-scripted '+' means 'positivist null hypothesis') with the alternative $H^{+}_{A}: \mu- 196 \ne 0$, then the corresponding 'negativist null hypothesis' is $H^{-}_{0}: |\mu- \mu_{0}| \ge \Delta$ (where $\Delta$ is the minimum relevant difference between the $\mu$ of your sample's parent distribution, and 196 that you would find meaningful. $\Delta$ is your choice as the researcher). The alternative to $H^{-}_{0}$ is $H^{-}_{A}: |\mu- \mu_{0}| < \Delta$. The absolute value bars mean this general negativist null hypothesis can be interpreted as two specific nulls:
$H^{-}_{01}: \mu- \mu_{0} \ge \Delta$; with $H^{-}_{A1}: \mu- \mu_{0} < \Delta$
$H^{-}_{02}: \mu- \mu_{0} \le -\Delta$; with $H^{-}_{A2}: \mu- \mu_{0} > -\Delta$
If you reject $H^{-}_{01}$, then you must conclude that $\mu - 196$ is less than $\Delta$. If you reject $H^{-}_{02}$, then you must conclude that $\mu - 196$ is greater than $-\Delta$. However, if you reject both If you reject $H^{-}_{01}$ and If you reject $H^{-}_{02}$, then you must conclude that $\mu - 196$ is on the interval between $-\Delta$ and $\Delta$… that is, you conclude that the difference is not big enough for you to care about.
The test statistic for $H^{+}_{0}$ is the familiar $t = (\bar{x} - 196)/s_{\bar{x}}$. You can construct two corresponding t test statistics for $H^{-}_{01}$ and $H^{-}_{02}$ thus:
$t_{1} = \frac{\left[\Delta - \left(\bar{x} - 196\right)\right]}{s_{\bar{x}}}$
$t_{2} = \frac{\left[\left(\bar{x} - 196\right) + \Delta \right]}{s_{\bar{x}}}$
I like to construct (and teach) the t test statistics this way, so that the direction of the tails when looking up p-values is unambiguous and unconfused: both of these t test statistics use upper-tail probabilities for $\nu = n-1$ degrees of freedom:
$p_{1} = P\left(T_{\nu} \ge t_{1}\right)$
$p_{2} = P\left(T_{\nu} \ge t_{2}\right)$
Both $H^{-}_{01}$ and $H^{-}_{02}$ are rejected at the $\alpha$ (not $\alpha/2$) level. Only if you reject both $H^{-}_{01}$ and $H^{-}_{02}$ do you reject $H^{-}_{0}$, and conclude that the difference must lie on the interval between $-\Delta$ and $\Delta$ at the $\alpha$-level of significance, and for the $\Delta$ threshold for relevance/equivalence.
One final thing: confirmation bias means that you only look for evidence in a single direction. So the hella dope thing to do is to look both for evidence of difference with the classical one-sample t test, and for evidence of equivalence with TOST, and to combine the results in your conclusions. Doing this is called relevance testing, and gives four possible results:
Reject $H^{+}_{0}$ and fail to reject $H^{-}_{0}$: conclude relevant difference.
Fail to reject $H^{+}_{0}$ and reject $H^{-}_{0}$: conclude equivalence.
Reject $H^{+}_{0}$ and reject $H^{-}_{0}$: conclude trivial difference (yes, there's a difference, but a priori you said you don't care about differences that small... in other words your test for difference is over-powered for your preferred effect size of $\Delta$, which may speak to the concern you mentioned about the very small p-value in the test for difference).
Fail to reject $H^{+}_{0}$ and fail to reject $H^{-}_{0}$: conclude indeterminate (your data are too under-powered to draw any conclusions one way or another).
References
Schuirmann, D. A. (1987). A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. Journal of Pharmacokinetics and Biopharmaceutics, 15(6):657–680.
