From 191382d3b8ca98d909186bd09813f91e2783208b Mon Sep 17 00:00:00 2001 From: Sharon Klinkenberg Date: Thu, 22 Feb 2024 11:50:42 +0100 Subject: [PATCH] Added nhst reflection topic --- .../2023-2024-S2/alternatives_nhst/Sad-P.webp | Bin 0 -> 279326 bytes .../alternatives_nhst/alternatives_nhst.qmd | 3 ++ .../alternatives_nhst.slide.html | 41 +++++++++++++++--- .../figure-revealjs/unnamed-chunk-4-1.png | Bin 107462 -> 80710 bytes .../figure-revealjs/unnamed-chunk-5-1.png | Bin 0 -> 107462 bytes topics/.DS_Store | Bin 20484 -> 20484 bytes topics/nhst_reflection/nhst_reflection.qmd | 23 ++++++++++ 7 files changed, 62 insertions(+), 5 deletions(-) create mode 100644 courses/SM/2023-2024-S2/alternatives_nhst/Sad-P.webp create mode 100644 courses/SM/2023-2024-S2/alternatives_nhst/alternatives_nhst_files/figure-revealjs/unnamed-chunk-5-1.png create mode 100644 topics/nhst_reflection/nhst_reflection.qmd diff --git a/courses/SM/2023-2024-S2/alternatives_nhst/Sad-P.webp 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b/courses/SM/2023-2024-S2/alternatives_nhst/alternatives_nhst.slide.html index 7cc20cb5..81a14216 100644 --- a/courses/SM/2023-2024-S2/alternatives_nhst/alternatives_nhst.slide.html +++ b/courses/SM/2023-2024-S2/alternatives_nhst/alternatives_nhst.slide.html @@ -362,6 +362,34 @@

Alternatives to NHST

2024-02-26

+
+

The problem with P-values

+ +
+
+

There is no problem

+

The problem with P-values is that they are often misunderstood and misinterpreted. The P-value is the probability of observing a test statistic as or more extreme as the one obtained, given that the null hypothesis is true. It is not the probability that the null hypothesis is true. The P-value is not a measure of the strength of the evidence against the null hypothesis.

+
+

The mis interpretation is the problem, and not adhering to the Nayman-Pearson framework

+
+
+
+

The dance of the P-value

+
+
+
+

H0 and HA distribution

+
+ +
+
+

Confidence Interval

The confidence interval is a range of values that is likely to contain the true value of an unknown population parameter. The confidence interval is calculated from a given set of sample data. The confidence interval is used to express the uncertainty associated with a sample estimate of a population parameter.

@@ -378,15 +406,15 @@

Standard Error

  • Upperbound = \(\bar{x} + 1.96 \times SE\)
  • -
    -

    Standard Error

    +
    +

    Plot CI

    -
    +

    5 out of 100 samples

    -

    +

    @@ -429,7 +457,7 @@

    Researcher don’t know

    Table 2 from Hoekstra et al. (2014) - + @@ -486,6 +514,9 @@

    Researcher don’t know

    +
    +
    +

    CI compare to H0

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zS5FE;X%Ff^is3`^uhly~7LX7~?8><}De}ySj(Ds&o7lPuyJpc0N?e}OmI$(X12sL> zQ*QuC?Q^t|R9PONII}x?y6v0MgRZ+ diff --git a/topics/nhst_reflection/nhst_reflection.qmd b/topics/nhst_reflection/nhst_reflection.qmd new file mode 100644 index 00000000..98919905 --- /dev/null +++ b/topics/nhst_reflection/nhst_reflection.qmd @@ -0,0 +1,23 @@ +# The problem with P-values {background-image="Sad-P.webp" background-color="black"} + +## There is no problem + +The problem with P-values is that they are often **misunderstood** and **misinterpreted**. The P-value is the probability of observing a sample statistic as or more extreme as the one obtained, given that the null hypothesis is true. It is **NOT** the probability that the null hypothesis is true. The P-value is **NOT** a measure of the strength of the evidence against the null hypothesis. + +> The mis interpretation is the problem, +> and not adhering to the Nayman-Pearson framework + +## The dance of the P-value + +* [Should replication reveal the same _p_?](https://youtu.be/ez4DgdurRPg?si=z7oIlKZx6iZjHNYH&t=58) +* [What Power are you using](https://youtu.be/ez4DgdurRPg?si=pN0QTEjARl_2mUO0&t=235) +* [Increasing the power](https://youtu.be/ez4DgdurRPg?si=QQku6BKu4C-8BvhF&t=396) +* [Comparing CI's to single point](https://youtu.be/ez4DgdurRPg?si=QPAcDeFmG-BUe8ZH&t=480) + +## H0 and HA distribution {.center} + +```{r tiny-effects, fig.pos='H', fig.align='center', fig.cap="Any effect can be statistically significant.", echo=FALSE, screenshot.opts = list(delay = 5), dev="png", out.width="1200px"} +# Illustrate that even tiny effects can yield statistically significant test results if the sample is sufficiently large. +# Generate a normal distribution as hypothesized sampling distribution (M = 2.8, SE = SD / sqrt(N) = 0.6 / sqrt(10) = 0.2) with 2.5% of each tail area coloured. Add a vertical line with value for the sample average linked to a slider (range [2.82, 3.00] initial value 2.90). Add a sample size slider (range [10, 5,000], initial value 10), which is linked to the standard error of the normal curve. With slider for (assumed) true population mean and test power. +knitr::include_app("https://sharon-klinkenberg.shinyapps.io/tiny-effects/", height="340px") +``` \ No newline at end of file