WEBVTT 00:00:00.490 --> 00:00:03.170 When we visualize data, it's usually a lot 00:00:03.170 --> 00:00:06.550 easier to draw conclusions. And we can see a 00:00:06.550 --> 00:00:10.260 clear upward trend in the relationship between hours slept 00:00:10.260 --> 00:00:13.740 and temporal memory. Based on this data, we can 00:00:13.740 --> 00:00:16.040 say that in general, the more you sleep 00:00:16.040 --> 00:00:19.450 the better your temporal memory score. This isn't to 00:00:19.450 --> 00:00:23.100 say though that sleep causes a higher temporal memory 00:00:23.100 --> 00:00:25.540 score. This is just the trend that we can 00:00:25.540 --> 00:00:28.320 see from this data. We don't really 00:00:28.320 --> 00:00:31.310 know if a higher temporal memory score translates 00:00:31.310 --> 00:00:33.880 to a better test result. So we can't 00:00:33.880 --> 00:00:37.560 say this second option. This third option is 00:00:37.560 --> 00:00:39.850 the opposite of the relationship we see here. 00:00:40.940 --> 00:00:44.120 And we definitely do see an upward trend 00:00:44.120 --> 00:00:47.540 in this relationship. We'll get more into visualizing 00:00:47.540 --> 00:00:51.440 data later. But for now, the point is, 00:00:51.440 --> 00:00:58.274 data comes in all forms. As raw data, or visualized, 00:00:58.274 --> 00:01:03.570 or in certain numbers that summarize the data. We should make good 00:01:03.570 --> 00:01:07.900 decisions on what methods we should use to draw sound conclusions from our data.